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Data acquisition and management software for camera trap data: A case study fromthe TEAM Network

Eric H. Fegraus a,⁎, Kai Lin b, Jorge A. Ahumada a, Chaitan Baru b, Sandeep Chandra b, Choonhan Youn b

a Tropical Ecology Assessment and Monitoring Network, Science and Knowledge Division, Conservation International, 2011 Crystal Drive, Suite 500, VA 22202, United Statesb University of California, San Diego, San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, CA 92093, United States

a b s t r a c ta r t i c l e i n f o

Article history:Received 3 February 2011Received in revised form 21 June 2011Accepted 21 June 2011Available online 2 July 2011

Keywords:Camera trapsWildlife monitoringData acquisition and management softwareGlobal monitoring networkCyberinfrastructure

Camera traps and the images they generate are becoming an essential tool for field biologists studying andmonitoring terrestrial animals, in particular medium to large terrestrial mammals and birds. In the lastfive years, camera traps have made the transition to digital technology, where these devices now producehundreds of instantly available images per month and a large amount of ancillary metadata (e.g., date, time,temperature, image size, etc.). Despite this accelerated pace in the development of digital image capture, fieldbiologists still lack adequate software solutions to process and manage the increasing amount of informationin a cost efficient way. In this paper we describe a software system that we have developed, called DeskTEAM,to address this issue. DeskTEAM has been developed in the context of the Tropical Ecology Assessment andMonitoring Network (TEAM), a global network that monitors terrestrial vertebrates. We describe thesoftware architecture and functionality and its utility in managing and processing large amounts of digitalcamera trap data collected throughout the global TEAM network. DeskTEAM incorporates software featuresand functionality that make it relevant to the broad camera trapping community. These include the ability torun the application locally on a laptop or desktop computer, without requiring an Internet connection, as wellas the ability to run on multiple operating systems; an intuitive navigational user interface with multiplelevels of detail (from individual images, to whole groups of images) which allows users to easily managehundreds or thousands of images; ability to automatically extract EXIF and custom metadata informationfrom digital images to increase standardization; availability of embedded taxonomic lists to allow users toeasily tag images with species identities; and the ability to export data packages consisting of data, metadataand images in standardized formats so that they can be transferred to online data warehouses for easyarchiving and dissemination. Lastly, building these software tools for wildlife scientists provides valuablelessons for the ecoinformatics community.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Data fromcamera traps havemanyscientific uses andhave becomeacritical tool for ecologists and conservation biologists. For example,camera traps have been used to estimate density via capture–recapturemodels with distinguishable individuals (Karanth et al., 2006), studyactivity patterns and habitat use (Bowkett et al., 2007; Rovero andMarshall, 2009), obtain information on species distribution and habitatuse (Silveira et al., 2003), identify new species (Rovero and Menegon,2008), determine species population structure and behavior (Karanthet al., 2006; Silveira et al., 2003), and measure trends in occupancy—asurrogate to abundance (Royle and Nichols, 2003). Recently, they havealso been used to estimate local animal densities (Kays et al., 2009;Rowcliffe et al., 2008; Royle and Nichols, 2003), which is the goldstandard for animalmonitoring. Furthermore, theWildlife Picture Index

(WPI) demonstrates how camera trap data, when deployed viastandardized monitoring protocols, can also be used as an indicatorthat is capable of detecting changes in the rate of loss of biodiversity formedium to large-sized terrestrial vertebrates in tropical forests andsavannahs (O'Brien et al., 2010).

Camera traps are an ideal instrument for scientific observation ofground-dwelling species because they are 1) relatively non-intrusivein the local environment, 2) many models now use infrared flashesthat do not affect animal behavior and 3) provide large quantities ofdata and metadata in image format (Kays et al., 2009). In the last fiveto seven years, camera traps have followed the photographic industrytransition to digital cameras. This technology transfer has resulted ina drastic increase in the use of camera traps and in the volume ofdata generated at numerous sites around the world (Ahumada et al.,in press; Rowcliffe and Carbone, 2008; Tobler et al., 2008). Based oninformation from the published literature and our own awareness ofon-going projects, we estimate that there are well over 125 cameratrapping projects around the world, run by NGOs, governmentagencies, and academic institutions, focused on animal monitoring

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⁎ Corresponding author. Tel.: +1 703 341 2482; fax: +1 703 979 2980.E-mail address: efegraus@conservation.org (E.H. Fegraus).

1574-9541/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.ecoinf.2011.06.003

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(with the total number possibly being much higher than that). Theuse of these sensors in national and international monitoringactivities and networks provides the potential to fill a large data gapfor the medium to large vertebrates living in tropical forests andgrasslands (Bailey et al., 2007; Collen et al., 2009; Kays et al., 2009; Lohet al., 2005).

However, the rapid increase in the use of camera traps has notbeen accompanied by a corresponding availability of good softwaresolutions to manage and analyze the image data and associatedmetadata (Harris et al., 2010). Capturing the necessary image meta-data that is required for scientific analyses is particularly challenging.For example, in the TEAM Network, standardized protocols have beenspecified for the field methodology, sampling design, census frequen-cy, sensor settings, calibration details and many other importantdetails. The software systemmust have checks in place to help ensurethat the protocols have been implemented in a standardized fashion,so that the camera trap data are comparable across space and time.Information such as, date-timestamps, and latitude and longitude ofthe photos is required, along with the specific field methodologicalinformation and custom camera EXIF metadata. All of this informationshould be properly documented and stored in a data managementsystem. Yet, there are very few systems available that are capable ofhandling the large volumes of images and the associated metadata.Furthermore, there are no global or community-based camera traprepositories or data warehouses to help store, analyze and dissem-inate this information (Rowcliffe and Carbone, 2008; Tobler et al.,2008). Here, we highlight a new software solution that incorporatesadvanced software functionality to enable the camera trapping com-munity to more fully and efficiently utilize and share camera trap data.We highlight this new functionality within the scope of the TEAMNetworks Terrestrial Vertebrate (TV) Protocol and describe a pathforward toward developing the technical and computational founda-tion required by the camera trapping community to continue to ad-vance their scientific endeavor. This approach to process largequantities of camera trap data and metadata further provides valuablelessons learned for the ecoinformatics community on building softwaretools for the wildlife sciences.

1.1. Challenges in collecting and processing camera trap data

Camera traps typically consist of ruggedized digital cameras withinfrared heat sensors and a battery pack. A growing list of othersensors are also being added (e.g., temperature, GPS, etc.). Cameratraps also include hardware and firmware that regulate the sensors,sensor settings and options, and control when and how the camerafires. The infrared sensor detects movement and causes the camera totake a photo. Many automated cameras can now be setup to providecustomized information in image EXIFmetadata headers (EXIF, 2010).Most camera trapping protocols require a minimum of 3 imagesper trigger (i.e., when a sensor detects movement the camera isprogrammed to fire 3 consecutive images) to ensure that the animalcan be identified. Other protocols require more images, e.g., in aneffort to gauge the velocity of the animal moving through the camerafield of view (Kays et al., 2009). With hundreds of triggers expectedwithin a month this can result in 250 to over a 1000 images percamera per month that need to be managed and identified. The TEAMNetwork, for example, requires each geographic location where itsTerrestrial Vertebrate protocol (i.e., camera trapping) is implementedto have a minimum of 60 camera trap sampling points deployed forat least 30 days. The expected number of images generated withinthe network in 2010 will be over 250,000, which will expand to over amillion images annually in the next two years. Another major con-cern is the availability of robust communication systems to transferall this information from the sites where it is generated to globaldata distribution systems or repositories. Many monitoring effortsthat use camera traps operate in developing countries, where Internet

bandwidth is usually narrow and unstable, impacting the ability toeasily transfer these data to shared repositories. With these concernsin mind, our primary objective with the DeskTEAM software is todevelop a software solution that facilitates not only the data acquisi-tion and management of digital camera trap images and metadata, butalso provides efficient mechanisms to share the data with back-endrepositories.

1.2. The TEAM Network

The Tropical Ecology Assessment andMonitoring (TEAM)Network(www.teamnetwork.org) is a partnership among Conservation Inter-national, The Missouri Botanical Garden, The Wildlife ConservationSociety and the Smithsonian Institution, which seeks to monitor longterm trends in the status of biodiversity and ecosystem servicesthrough a network of tropical field stations (TEAM Sites) using scien-tifically accepted standardized monitoring protocols. The TEAMNetwork monitors biodiversity and a set of ecosystem services usinga relatively small suite of standardized protocols, including Climate,Vegetation, and Terrestrial Vertebrate (TEAM, 2008, 2010a, 2010b),which facilitate comparisons among sites and continents using metricsthat quantify changes in climate, land use/land cover, biodiversity andcarbon sequestration.

1.3. TEAM cyberinfrastructure

Over the past four years, the San Diego Supercomputer Center atUC San Diego has been working in close partnership with Conserva-tion International, to develop the comprehensive cyberinfrastructureneeded for the TEAM Network to support its full range of activities.Various components of the TEAM cyberinfrastructure are depictedin Fig. 1, including data collection via sensors, EcoPDAs, and theDeskTEAM software; tools to assist with TEAM Operations; and avariety of applications that use and serve the TEAM data. A key func-tion of this cyberinfrastructure is to provide the site and headquartersstaff a variety of tools for data collection, data curation, and qualityassurance/quality control (QA/QC) that enforce the various networkprotocols, thereby helping ensure the quality of data being collected inthe network. The system is constantly evolving: new sites may beadded to the network, data collection technologies aswell as protocolsmay evolve over time, and the networkmay adopt new protocols. Thecyberinfrastructure is designed to carefully track this evolution byclearly recording protocol versions under which data are collected andproviding that information as metadata whenever users downloaddatasets from the TEAM Portal.

2. Materials and methods

The camera trap software requirements are best illustrated incontext of a monitoring protocol. Here we describe in detail the TEAMTerrestrial Vertebrate Protocol, which is implemented in every TEAMNetwork site.

2.1. The TEAM Terrestrial Vertebrate (TV) Protocol

Each site in the TEAM Network implements the TerrestrialVertebrate Monitoring Protocol (i.e., camera trapping protocol). Fig. 2shows a segment of the TV Protocol workflow for the first year ofdeployment at a given site. In a nutshell, each site deploys at least 60camera trappoints during thedry season for at least 30 days of samplingper point (minimum effort is 1800 camera trap days). The points do notneed to bedeployed simultaneously, but canbedeployed sequentially inbatches or arrays of 20–30 points. Camera trap points are laid outregularly in a grid with a density of 1 camera trap every 2 km2. A givencamera trap sensormay be used inmany camera trap points, i.e. cameratrap sensors can move among camera trap points during the year. The

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serial number of the camera sensor is recorded to track which cameratrap sensor was in which camera trap point at a given time. Lastly, aschedule is created that clearly identifies when each camera trap pointsis supposed to be deployed.

2.2. Field deployment

During initial deployment, TEAM sites must ensure all camera trappoint spatial locations are recorded. This may be done via special fieldcampaigns, or while acquiring the data during the first year of datacollection, according to the approved sampling schedule. Once all ofthe spatial information is collected and verified, the actual cameratrap sensors can be deployed. This entails mounting the cameras to asturdy object (typically a tree), placing it at a certain height and angleto ensure that the full camera lens field of view is maximized, andfinally, turning on the camera and making the final settings that couldnot be done earlier at the field station. A few test photos are taken(Fig. 3), which include one with the technician holding a white boardwith the sampling unit ID and date written on it, to associate allimages to that sampling unit. This additional step is to help ensurethat the field personnel have correctly configured the camera trapsensors, by associating it with the sampling unit ID shown on thewhite board.

Camera trap sensors are configured via the programmable interfacesprovided by each camera trap manufacturer. The list of configurationsand options is highly customized to each manufacturer. The cameracurrently in use at all sites in the TEAM Network has controls for:activating the infrared heat sensor; sensitivity adjustments; picturesper trigger; a “no picture” quiet period after a trigger; image resolution;whether to turn on temperature sensor; date and timestamp format;and many other options. All of the settings and options are written intothe image EXIF metadata tags (Reconyx, 2008), ensuring that thecamera settings will remain with the image. The settings and othermetadata are vital to ensure the integrity of the image.

2.3. Data collection

Once deployed, the cameras are left to run in the field for the desiredsampling length, viz. 30 days. Before collecting the cameras the

technicians again use white boards to capture the date/timestamp andlocation of the camera trap point. At the lab or field station, thetechnicians interpret each image anddetermine the species andnumberof individuals in the image (using the software solution described inSection 3). When this task has been completed for all the images, thedata are ready tobe transferred to the central TEAMdata repository. Thismay require transfer of several gigabytes of data each time from TEAMsites, which are typically in remote locations with limited Internetbandwidth. In practice, the transfer can take many hours, or even days,and in many cases is simply not feasible, given the current agreementswith local Internet service providers. The most reliable method,therefore, is to ship data (images, data and metadata) via removablemedia, e.g., CD,DVDorUSBdrive to the TEAMNetwork office for loadinginto the information system. Once in the system, data are available forcuration and distribution to network members and the public.

2.4. User testing and gathering software requirements

As with any software, user testing and feedback are vital to ensurethe software is adopted by the intended users. Our software needed tosolve an intractable problem (i.e., manually annotating 1000s of imagesin a short-time period) so was highly desired and eagerly awaited. TheTEAM Network staff developed many of the initial requirements alongwith informal input from many camera trapping experts within andoutside of the TEAM Network. To further gather requirements a testingkit was created that contained a functioning prototype, instructions, aconfiguration file and a “canned” data package to simulate data comingfrom a camera trap. The canned data package contained real cameratrap images from one of our camera trap pilots to make the experienceas real as possible. The testing kit was circulated as widely as possible(also both within and outside of the network). This testing experienceprovided invaluable information on user tendencies, requirements andimproved workflow. An alpha version was created from thesecomments. We then proceeded in an iterative manner to produce aproduction release to ensure the main GUI and the annotation processwas intuitive for all types of users. The intended users of the softwarehave varied backgrounds ranging from para-biologists (i.e., local peoplewith less than a high school education but trained inwildlife biology) todata managers with some IT background to PhD level scientists.

Fig. 1. TEAM cyberinfrastructure. The TEAM cyberinfrastructure is an end-to-end data management and dissemination solution for TEAM Network data.

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The requirements collected throughout these experiences ulti-mately had to vastly improve upon the status quo of managing cameratrap images which included analyzing images with standard desktopimage viewing software, transcribing field notes to a custom data file(i.e., similar to Microsoft Excel) and appending to this file the im-portant annotation information from the image to each record. Thekey software requirements that are needed to satisfy the TEAM Net-work Terrestrial Vertebrate protocol are the following:

• A local, desktop application that can export secure data packages(including the data, metadata and images) to the local file system aswell as to removable media

• Support for multiple OS platforms (Windows XP, Windows 7, MacOS X, various Linux flavors, etc.) and for access by users in differentorganizations

• The ability for the local desktop application to sync with the centralinformation server so that: 1) sampling units, personnel, serialnumbers, taxonomic lists and other information can be updated andassigned for each TEAM Site, 2) updates to the software applicationitself canbeperformedeasilywithout requiringamanual re-installationprocess with each new release, 3) where there is good Internetconnectivity, the ability to directly submit data packages to the centralinformation server (located at SDSC)

• An intuitive navigation and user interface to easily identify the setupand pick-up photos, unknown photos, the person who identified thephoto and most importantly the capacity to easily group and identifymany images at once. Each TEAM processes hundreds to thousands ofimages per trap (~50,000 images per TEAM site per year has been thehighest total to date).

• Capacity to collect the important metadata associated with the fieldmethodology as well as EXIF imagemetadata. This includes not onlystandard EXIF image metadata but also custom tags by the cameramanufacture (e.g., temperature sensor reading, moon phase,firmware version, date-time stamp, camera trap settings, etc.).

• Ensure that the data packages are complete when exported andstandardized so that they can be archived in the information man-agement system

• Ensure that the information system can process and track submitteddata packages, sync with the desktop application and the customversion for each TEAMSite, provideweb-based tools to edit and curatesubmitted data and lastly, disseminate the images, metadata and datavia the TEAM Network portal as close to the time of submission aspossible

3. Results

3.1. DeskTEAM: software design and engineering

DeskTEAM is a desktop application designed for managing cameratrap data and is being used throughout the TEAM Network at 16 TEAMSites and over 50 users. In addition, over 250,000 images are processedeach year with the volume expected to increase as the network grows.This represents the largest camera trapping repository of tropical forestvertebrates. The software provides authenticated and role-based accessto forms that can be used to uploadand editfieldmetadata anddata. Thedata canbe synchronizedwith the central TEAMdatabase by connectingto the Internet, or packaging data into a file and shipping it to the TEAMNetwork office. The system is specifically designed to accommodateTEAM sites that do not have good Internet connectivity and requiresvery little manual intervention for installation. The application containsall the necessary support software packages (e.g., web services stack,database software) and does not require installation of any additionalsoftware. The network office manages the release of DeskTEAM. Fig. 4defines the DeskTEAM workflow.

A DeskTEAM user can download the latest DeskTEAM softwarepackage from the TEAM portal. The software is installed by executing

Fig. 2. TEAM Terrestrial Vertebrate Protocol workflow. A complex series of iterativesteps is required to accurately setup and deploy a data campaign.

Fig. 3. Camera trap setup image using whiteboard. An example image from one of theTEAM Sites.

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the installer. The new installed DeskTEAM needs to be configured byloading a configuration file for a specific TEAM site to populate itsinternal database and work for that site. The configuration file canalso be downloaded from the TEAM portal if the user has appropri-ate privileges to access it. A configuration file contains a sequence ofencrypted SQL statements, which save detailed information about asite, serial numbers of camera traps, the team members working atthe site and the sampling schedules into the local database. Thisinformation is managed and entered into the central TEAM databaseby the site manager.

After loading the configuration file a DeskTEAM user can startprocessing the images collected by the camera traps deployed at theirsite. A camera trap name and other metadata must be provided eachtime the user imports the image files from a memory card into theDeskTEAM. The software looks for attachedmedia (e.g., memory card)and allows the user to select the appropriate drive. The user is ableto annotate the imported images by looking at them through theDeskTEAMGUI either individually or in a groupmode. After all imagesfrom an array of camera traps are imported and annotated, they canbe exported as a DeskTEAM package and sent to the network office. ADeskTEAM package is an encrypted zip file containing the followingitems:

1. For each camera trapa. a folder for its imported imagesb. an XML file for all metadata of photos and their annotations

2. A manifest file for package level metadata

Once transferred to the network office the DeskTEAMdata packagesare opened, and the data are extracted and saved to the central databaseand image repository.

The DeskTEAM user can update the installed DeskTEAM at anytime by executing the update utility provided by DeskTEAM. It willupdate the DeskTEAM software itself and the configuration using thelatest information available from the TEAM portal. If needed, a new

DeskTEAM configuration file can contain commands for updatingthe local database schema to synchronize with the central databaseschema.

3.2. Architecture of DeskTEAM

Fig. 5 shows the architecture of DeskTEAM. Each component isdescribed below:

• The DeskTEAM data model is the conceptual model of the dataprocessed by DeskTEAM, and is described in detail in Section 3.3.Themodel is implemented as a set of persistent entities andmappedinto the DeskTEAM database. The DeskTEAM Initial Model is a sub-set of the data model, containing the minimum set of entities andattributes required for using DeskTEAM.

• The DeskTEAM database and image repository is the basic storagesystem consisting of an image repository for saving images, and aninternal database for storing image related metadata. The internal

Site Manager Network OfficeDeskTEAM User

Enter site members andcamera information

Release DeskTEAMmajor version

Download and installDeskTEAM

Download and loadthe site configuration

has unloaded memory cards

all memory cards are loaded

Load photos froma memory card

all photos areannotated

has photosnot annotated

Annotate photos

Export and senddata package

Save data package into database

Release DeskTEAMminor version

Update site members and camera information

Update DeskTEAM

Fig. 4. DeskTEAM workflow. The framework from which DeskTEAM was implemented.

Data Import Data Export

Configuration

DeskTEAM Update

Image Annotation Damage Report

DeskTEAM Data Model

Initial Model

DeskTEAMdatabase

ImageRepository

Fig. 5. DeskTEAM architecture. The architecture used by DeskTEAM to satisfy the TEAMNetwork TV Protocol.

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database is implemented using an embedded MySQL database,which can be deployed on a broad range of platforms.

• The configuration module is invoked when DeskTEAM is used for thefirst time. It prompts the user to select a DeskTEAM configuration file,which initializes the DeskTEAM database. The configuration fileprovides the initial model, containing all necessary setup and initialconfiguration information, e.g. sampling unit information, samplingschedule, etc. for data collection. The configuration module could beinvoked at any time, and each time, it will replace the existing initialmodel with information provided for a new initial model.

• Data import. This module supports the importing of images frommemory cards. The user is required to enter theminimummetadata,for example, the sample event name (name of the event, e.g. WetSeason 2010), date and time of data collection, and names of thepersonnel picking up the camera trap. The module copies all imageson the memory card into the local repository, and extracts allrequired metadata from each image header and saves the metadatain the local database in DeskTEAM. DeskTEAM also supportsloading/importing DeskTEAM data packages, which can be createdusing the export function described below. This capability allowsusers to exchange camera trap data and collaborate on collections ofcamera trap images (i.e., multi-user, multi-computer functionality).

• Damage report. This module deals with damaged camera trap data. Acamera trap may be partially damaged, i.e., it has a readable memorycard, but the device has malfunctioned prior to its scheduled pick up,so it may be missing data for some part of the sampling period. Thissituation is detected by the module and reported after the memorycard is imported. The damage information is reported and recordedin the database. A completely damaged camera trap is one with anunreadable memory card. This information is also recorded in thesystem, indicating that there are no data for a given sampling perioddue to damaged memory card.

• Image annotation. Images imported into DeskTEAM can be annotatedusing one of the following:

o Setup/pickup: images takenwhen setting up or picking up camerastraps

o Start: image indicating starting times of the camera traps for a givensampling event. For a given sampling event and a camera trap, onlyone start image can be specified.

o End: the image indicating the end times of the camera traps for agiven sampling event. For a given sampling event and a cameratrap, only a single end image can be specified.

o Blank: images without animalso Unidentifiable: images with animals, but impossible to determine

taxonomyo Unknown: images with unknown species to the identifiero Animals: images with animals whose names are listed in the

taxonomic authoritiesOne or more species names can be associated with an image. Tospeedup the image annotation process, DeskTEAM automaticallyputs similar images and images captured within a close timewindow (b1 minute) into the same groups. An annotation appliedto an image can also be applied to the entire group to which thatimage belongs. New groups can be created from existing ones, forexample, by removing some images.

• Data export. Given a camera trap array and a sampling period, allassociated images with their metadata and annotations can be ex-ported to afile. Thisfile is called aDeskTEAMpackage. EachDeskTEAMpackage contains a manifest file that shows the package status. Forexample, themanifest indicateswhether all the images in the packagehave been annotated or not. An application can open a DeskTEAMpackage and obtain its status by reading the manifest file.

• DeskTEAM update. The DeskTEAM update module facilitates soft-ware configuration management in the highly distributed TEAMNetwork. The module enables DeskTEAM software at any network

site to be updated “on the fly” by downloading the latest version ofthe software from the TEAM portal and replacing the older ver-sion. Also, the configuration module is invoked to reinitialize thesoftware.

3.3. The DeskTEAM data model

Fig. 6 depicts the main objects in the DeskTEAM data model. Thisprovides an abstraction of the camera trap data including conceptualobjects and operations and constraints on these objects. While notshown in the figure, the DeskTEAM data model also models the sys-tem configuration, software versioning, and other historical informa-tion to facilitate data management, search, and retrieval. Thus, itprovides the semantics and structure of DeskTEAM data and is criticalfor implementing the DeskTEAM system and other associated infra-structure services.

The main objects shown in the figure are:

• Camera trap: a basic unit used in recording images. Each cameratrap must have a unique ID and latitude and longitude. When acamera trap is deployed or picked up from the field, a date, personand a memory card should be associated with it.

• Damaged camera trap: is a camera trap that was not working at thetime when its memory card was picked up. It has attributes linkingto various possible reasons for the damage.

• Camera trap array: is a set of camera traps. The camera trap array isthe minimum unit for exporting data as DeskTEAM packages.

• Memory card: represents a specific memory card used in a cameratrap for collecting images for a given sampling period; it is associatedwith one and only one camera trap.

• Sampling event: represents a time range in which camera traps aresupposed to collect data. Each sampling event has a unique nameand a start and end time.

• Person: persons can appear in several roles. They can setup a cameratrap, pickup a camera trap, recognize species on images, etc. When aDeskTEAM package is created, the name of the package creator isalso recorded in the manifest file of the package.

• Photo collection: represents the entire image set on a memory card.Each photo collection is linked to a memory card for a given samplingevent.

• Photo: represents an item in each photo collection.• Animal: represents an object in the taxonomy authorities. One ormore animals can be attached to a photo.

The DeskTEAM data model is currently implemented as Java EntityBeans using Hibernate to provide persistence support. Also JAXB isused to marshal and unmarshal these entity beans to or from the XMLformat. This model will serve as the basis of the initial Camera TrapData Model (CTDM) discussed in Section 4.1.

Camera TrapArray

Camera Trap

Person

Memory Card

Animal

Photo

Photo Collection

Sampling EventDamagedCamera Trap

has has

has

is

use

setup/pickup

from

recognize

for

Fig. 6. DeskTEAM data model objects. Key objects within the DeskTEAM data model.

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The DeskTEAM Initial Model mentioned earlier contains the objectsshown in Fig. 7, which includes a set of camera traps, a set of persons, aset of sampling events, and a set of taxonomic authorities (representedby the Animal entity). This minimum information is required to ini-tialize and start using DeskTEAM.

3.4. DeskTEAM software implementation

The DeskTEAM software is implemented in Java. Other key softwarecomponents includeApacheEmbeddedTomcat,Hibernate3 (for object-to-tablemapping), Jaxb 2 (for object-to-xml mapping in the TEAM datamodel xml schema), and Java Cryptography Extension to encrypt andcompress configuration files and export packages. To support the use ofDeskTEAM components in other web applications, the application isdesigned to run within web browsers, thus users can access the entireDeskTEAM functionality via a browser. When DeskTEAM is started, anembedded Tomcat and an embedded MySQL database are launchedalong with it. Correspondingly, when DeskTEAM is closed, theembedded Tomcat and MySQL servers are also shut down. For securityreasons, the system is configured to allow only local access to theembedded Tomcat and MySQL. DeskTEAM will work on any platformthat supports Java and MySQL. The current DeskTEAM application hasbeen tested on Windows and Mac OSX platforms, and with Firefox,Internet Explorer, and Safari browsers. Fig. 8 shows a screen snapshot of

DeskTEAM, which shows the memory card contents in the left-handpanel, a specific image in themiddle panel, the image annotation area aswell as some of the standard and custom EXIF metadata in the rightpanel, and sampling metadata in the bottom panel. All of theinformation except for the few user entered fields in the annotationwindow are populated by the configuration file, camera trap setupinformation, or read directly from the imagemetadata. This high degreeof automation removes human error in the capture ofmost of thedata inthe image, leaving only the species identification to the user. Even here,the user is presented with a choice of standardized scientific names forthe species so no typological errors are introduced in the data. Thesoftware is simple to install and contains embedded help information.Very little training is needed to some of the key functionality providedby DeskTEAM described below.

DeskTEAM configuration files: DeskTEAM allows users to updatethe initialmodel at any time, asneeded.Updatesmaybe done via theInternet, by downloading a configuration file from a predefined URL,or by using a local configuration file. As described earlier, theconfiguration file encodes the initial data model using a predefinedXML format. These configuration files, similar to DeskTEAMpackages, are encrypted using a public key so that they cannot beopened, accessed, or modified by other applications.Data quality control: DeskTEAM performs a set of data validationsbased on constraints specified in the DeskTEAM data model. Forexample, when amemory card is loaded into DeskTEAM for a givencamera trap and a sampling event, the system will make sure thatthis memory card is only used by one camera trap for this samplingevent. The timestamp of each image is also extracted from theimage header to compare with the time range of the samplingevent. The image will be flagged if it is out of the range of thesampling event.Grouping images: To accelerate image annotation, DeskTEAM canorganize and display images in groups based on their timestamps

Camera TrapArray

Camera Trap

Person

Memory Card

Animal

Sampling Event

has

Fig. 7. DeskTEAM Initial Model. The minimum information to initialize DeskTEAM.

Fig. 8. DeskTEAM screen shot illustrating the key windows, GUI and functionality of the current software. Note the image annotation area on the right. This contains the only fieldsthat the user needs to modify to annotate an individual image or a group of images.

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and image similarity. For a given image and group, the image isincluded in the group only if one of the following conditions issatisfied:

1. The timestamp of the image is very close (b10 seconds) to thetimestamp of any image in the group.

2. The timestamp of the image is close (b1 minute but N10 seconds)to the timestamps of an image in the group and their grayscalesimilarity is very high.

An annotation made to a single image in an image group can alsobe applied to all images in the entire group. This facilitates quickannotation of a sequence of images that were triggered by a particularanimal or group of animals. Another annotation functionality is to“remove” and image from a group. For example, a blank imagemay beincluded in a group because the animal(s) that was presentmoved outof the view of the camera trap. DeskTEAM provides the capability toexclude these images from a group. The annotations applied to thegroup will not apply to the excluded image(s). Indeed, processing1000s of images can now be done in a day while ensuring high qualitybecause the sources of error are controlled throughout the system.

3.4.1. Image annotationOnce the images have been grouped to the user satisfaction, he/she is

thenpresentedwith several choices to annotate the image. The image canbe annotated through different fields. A key and important feature ofDeskTEAM is that the annotation of genus and species for the images isconstrained by pre-approved binomials from a standardized taxonomicauthority.

3.4.1.1. DeskTEAM packages. Once all images have been annotated for agiven site and sampling season, DeskTEAM allows users to export datainto a DeskTEAMpackage. The package is a zipped file that contains allimages and related metadata in a predefined XML format. To retainthe integrity of the data collection, DeskTEAM packages use a publickey to encrypt the zipped files so that they cannot be opened by anyother applications to modify their data manually.

4. Discussion and conclusion

The DeskTEAM data acquisition and management software accom-panied by our web-based data warehouse and distribution portalprovides a complete end-to-end data management solution for theTEAM Network TV protocol data. These tools allow ecologists andwildlife managers to manage images along with their associatedmetadata from the camera trap, the image and the scientific protocolused to collect the data. However, this model is extensible to othercamera trap models and camera trapping efforts. In fact DeskTEAMversion 2.0 has already incorporated additional camera trap models(eight Reconyx camera trap models are now supported).

4.1. Generalizing the software

The camera trapping community could be able to benefit from thesignificant initial software development effort that has already beenundertaken in the TEAM Network project. For the software to functionoutside of the TEAMNetwork TV protocol the communitywould need (i)a generalized, open source version of DeskTEAM software, (ii) a simplerepository API and corresponding implementation for server-side storageof camera trapdata, including anoption to store the data in theCloud, e.g.,using Flickr or other such systems, and, (iii) importantly, develop aspecification of a Camera Trap Data Model (CTDM) specification, tofacilitate exchange of camera trap data between desktop and server, andamong different camera trap repositories, with a corresponding XMLimplementation of the data model.

The software would be designed to be extensible so that othercamera trapping projects can utilize the DeskTEAM softwareinfrastructure but configure it for their specific needs. An open sourceversion will need a local data repository/database for storing imagesand their metadata, and a simple server-side API for storing theimages at a remote server. In addition, the following functionalityshould be included in future software development efforts:

Configuration—the software should be appropriately customizedand configured for a given camera trap type (i.e., make andmodel),and to populate its initialmodel once it is installed. This can be donethrough the Internet or a local GUI. Ideally, a configuration file inXML format is defined by the CTDM and provides the followinginformation: the camera trap and its attributes; camera trap layout;teammembers and their roles; sampling events for data collection;and taxonomic authorities for animal identification, for use in dataannotation and validation.Image annotation—a flexible editor should be implemented toefficiently annotate large image collections. Image recognitionsoftware can be tested to determine its efficiency on recognizingindividual species. Ideally users could also be able to add or removeany image to/from a group, and apply a species name to all theimages in a group. The system could provide the ability to plug-inother image “reorganization” algorithms so that projects may usecustomized ways of organizing their images.Server side support—a web-based data warehouse that supportsan open source community would need to take advantage of anexisting imagery repository. For example, a server-side API thatsupports saving images into a cloud repository, such as Flickr orPhotoBucket, could be developed. The server-side API can beembedded in an application and/or portal to extract data from aCTDM-based XML package and save the metadata and data in localproject-specific databases.Analytical support—an analytical module built directly into thesoftware application and/or a web-based layer on the server sidemay be useful for many scientists. Example capabilities includeproviding data summaries and reports, capacity to export data inparticular formats required for other software packages to doanalyses or conducting analyses related to occupancy, abundanceestimates and density from within the environment.

4.2. The Camera Trap Data Model (CTDM)

To support the broader camera trapping community, the Desk-TEAM data model should be extended to the Camera Trap Data Model(CTDM) to support the following capabilities.

4.2.1. Many different camera trap makes and modelsThe current DeskTEAM only supports the camera traps imple-

mented by the TEAM Network. Higher resolution cameras requireeach camera to have slightly different settings with respect to itsshutter speed and field of view; improved heat sensors may changedetection distance and sensitivity settings as well as reduce theneed for flash; there may be additional sensors on board, such astemperature and GPS; and, the device may have settings to optimizefor power. In addition when loading images from a memory card,DeskTEAM knows how to find the memory card based on the well-known folder hierarchy for particular camera trap models. To supportother types of camera traps, CTDM should make this aspect con-figurable, to specify a new camera trap type and its default folderstructure.

4.2.2. Flexible metadata extraction from image headersA camera trap has settings that define the attributes in all images it

generates. This varies between different makes and models of cameratraps. These attributes are very useful for searching and processing

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data. CTDM should allow this list of attributes to be configurable basedon camera trap type, so that the software can extract them whenloading a memory card.

4.2.3. Different field methodologies and sampling designs (i.e., both thespatial and temporal components) to organize camera traps

The current DeskTEAM supports the specific TEAM TerrestrialVertebrate Protocol, which requires deploying at least 60 camera trapsinto pre-defined, labeled arrays (usually 2–3 per TEAM Site). CTDMshould introduce a camera trap layout object to provide support fordifferent sampling designs. This camera trap layout will let usersspecify their particular field layout of camera traps. Similarly all thecamera traps are activated for 30 days once per year. The activationlength and sampling frequency may well vary for camera trappingprojects. The CTDM should be able to accommodate changes to all ofthese field methodology and sampling designs.

4.2.4. Roles of team membersThe CTDM should provide a general model for specifying team

members and their roles with respect to the protocol elements. In thecurrent DeskTEAM software, the roles cover activities such as cameratrap “setup” and “pickup,” and “image annotation.” The CTDM shouldinclude the ability to define such roles and their associated permissionswithin CTDM.

4.2.5. More generic camera trap damage processingThe current DeskTEAM application has a specific way of handling

damaged camera traps. Different projectsmight choose other strategies,leading to the definition of new objects and their relationships in CTDM.

4.2.6. Server side data collection/validationWhen data packages are created and sent to an organization, some

validation must be done before saving the data into the centralrepository. Validation rules, in the form of data constraints, should beincluded in the CTDM to ensure the basic quality of received data.

The DeskTEAM software provides wildlife scientists within theTEAMNetwork a tool to work with digital camera traps, thus solving apreviously intractable problem stemming from the relatively new useof digital cameras and the large amounts of data they produce. Wehave outlined the next steps to enable our software to work with anycamera trapping effort and have posted the source code at http://code.google.com/p/deskteam/. Furthermore, we feel many of thetechniques demonstrated in this software, ranging from automatedstoring of standard and custom EXIF metadata, generating customconfiguration files and the capability to sync the software with ourcentral system greatly advance the ecoinformatics tools available forecological data acquisition and management.

Acknowledgements

The software development, tools and this paper were madepossible by the TEAM Network, a partnership between ConservationInternational, The Missouri Botanical Garden, The SmithsonianInstitution and TheWildlife Conservation Society, and partially fundedby these institutions and the Gordon and BettyMoore Foundation. TheDeskTEAM software received many suggestions and incorporatedfeedback from many TEAM Network members and we thank themvery much for their constructive input.

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