Illumination-Sensitive Background Modeling Approach for Accurate Moving Object Detection

794 IEEE TRANSACTIONS ON BROADCASTING, VOL. 57, NO. 4, DECEMBER 2011

Illumination-Sensitive Background ModelingApproach for Accurate Moving Object Detection

Fan-Chieh Cheng, Shih-Chia Huang, and Shanq-Jang Ruan, Member, IEEE

Abstract—Background subtraction involves generating thebackground model from the video sequence to detect the fore-ground and object for many computer vision applications,including traffic security, human-machine interaction, objectrecognition, and so on. In general, many background subtractionapproaches cannot update the current status of the backgroundimage in scenes with sudden illumination change. This is especiallytrue in regard to motion detection when light is suddenly switchedon or off. This paper proposes an illumination-sensitive back-ground modeling approach to analyze the illumination changeand detect moving objects. For the sudden illumination change,an illumination evaluation is used to determine two backgroundcandidates, including a light background image and a dark back-ground image. Based on the background model and illuminationevaluation, the binary mask of moving objects can be generated bythe proposed thresholding function. Experimental results demon-strate the effectiveness of the proposed approach in providing apromising detection outcome and low computational cost.

Index Terms—Background model, object detection, sudden illu-mination change, video surveillance.

I. INTRODUCTION

V IDEO surveillance systems have significant implicationsin the defense against criminality and terrorist threats in

both public and private sectors. In the design of an advancedvideo surveillance system, motion detection is usually a criticalprocess responsible for determining the locations of moving ob-jects. Furthermore, motion detection has been used for manycomputer vision applications, including driver assistance [1],human-machine interaction [2], face detection [3], collision pre-diction of pedestrians [4], remote image processing [5], detec-tion of foreign bodies in food [6], event recognition of humanactions [7], and so on.

According to a survey of related literature [8], motion detec-tion approaches can be broadly classified as three categories:temporal difference [9], [10], optical flow [11], [12], and back-ground subtraction [13], [14]. Temporal difference approachesdetect moving objects by calculating the differences betweenpixels in consecutive frames of a video sequence. Althoughtemporal difference approaches are adaptive to environmentswith sudden illumination change, some relevant pixels cannotbe extracted; this results in holes inside moving entities. Optical

Manuscript received January 26, 2011; revised April 14, 2011; accepted June07, 2011. Date of publication July 25, 2011; date of current version November23, 2011. This work was supported by the National Science Council under theGrant NSC 99-2221-E-027-108 and NSC 99-2221-E-011-145.

F.-C. Cheng and S.-J. Ruan are with the Department of Electronic En-gineering, National Taiwan University of Science and Technology (e-mail:[email protected]; [email protected]).

S.-C. Huang is with the Department of Electronic Engineering, NationalTaipei University of Technology (e-mail: [email protected]).

Digital Object Identifier 10.1109/TBC.2011.2160106

flow methods usually use characteristics of flow-vectors overtime to indicate moving regions in a video sequence. However,flow-vectors of moving objects only can be applied to illustratestreams of moving objects, thus detecting a sparse form ofobject regions. Out of these three categories, background sub-traction received the most attention due to its computationallyaffordable implementation and its accurate detection of movingentities.

In general, moving objects can be detected by the backgroundsubtraction approaches [13], [14] which consist of the back-ground model and the object thresholding function. However,the generated background model may not be applicable in somescenes with sudden illumination change. As a result, it is highlydependent on a good background model to reduce the influenceof these changes. Therefore, we propose an illumination-sensi-tive background modeling approach based on an illuminationevaluation in order to generate an accurate background modelfor motion detection. The remainder of this paper is organizedas follows: Section II describes some representational motiondetection approaches that are implemented in our experiments.Section III presents our approach in detail. In Section IV, wepresent the experimental results and discussions. Finally, ourconclusion is provided in Section V.

II. RELATED WORK

In this section, we describe three representative motiondetection approaches, including a multiple temporal differenceapproach (MTD) [10], a simple statistical difference approach(SSD), and a multiple estimation approach (MSDE) [14].Note that each approach is described by assuming thatrefers to an original video frame with size , whererepresents the frame index and represents the coordinateof each pixel.

A. MTD Approach

The MTD approach [10] doesn’t maintain a backgroundmodel but holds several previous reference frames to reduceholes inside moving entities for motion detection. Accordingto the literature [10], there are seven previous reference framesbe used to calculate the difference image. The previous framescan be held by the following function:

(1)

where represents each reference frame, represents theindex of reference frame, and represents the short termcounter which should be increased progressivelyat each frame. Note that both and are initialized to zero.Furthermore, and should be reset to zero when is

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CHENG et al.: ILLUMINATION-SENSITIVE BACKGROUND MODELING APPROACH 795

greater than , which is empirically set to six according tothe literature [10].

After generating the reference frames, the absolute differenceimage can be calculated as follows:

(2)

The binary mask of moving objects is calculated as follows:

if ,otherwise,

(3)

where equals 1 to represent a motion pixel and equals0 to represent a background pixel.

B. SSD Approach

The SSD approach consists of a simple background model bytemporal average and an object thresholding function by tem-poral standard deviation. For each pixel, the temporal average

and temporal standard deviation are calculatedas follows:

(4)

(5)

Note that both and are initialized to zero. Basedon the simple background model by equation (4), the absolutedifference image can be calculated as follows:

(6)

The binary mask of moving objects is calculated as follows:

if ,otherwise.

(7)

where represents the pre-defined parameter. According to theliterature [13], is empirically set to 3 yields much better re-sults.

C. MSDE Approach

The MSDE approach consists of a hybrid background modeland an object thresholding function, which are both formulatedby traditional sign function. Before calculating the hybrid back-ground model, the different reference images must be generatedat each frame. When the frame index is a multiple of , thedifferent reference images can be formulated as follows:

(8)

where represents the current frame of -th referenceimage, represents the previous frame of -th refer-ence image, and represents the current frame of

-th reference image. Note that is regarded as originalvideo frame . Additionally, weighted variances can becalculated as

(9)

where represents the absolute difference betweenand , represents the current frame

of -th weighted variance, represents the previousframe of -th weighted variance, and represents the pre-de-fined parameter which is empirically set to two [14].

Based on the use of reference images and weighted variances,the hybrid background model can be calculated as

(10)

According to the literature [14], is experimentally set to threeand confidence values to are set to 1, 8, and 16, respec-tively. Finally, the binary mask of moving objects canbe generated by the same thresholding function of SDE ap-proach that uses the first weighted variance for binarization.

D. Discussion

Since the MTD approach uses several temporal reference im-ages to detect moving objects and adapt to sudden illumina-tion change, holes are reduced inside moving entities of the bi-nary mask. However, the detected objects may drag ghost arti-facts due to use of several consecutive frames possibly involvingmoving objects.

Compared with the MTD approach, the SSD and MSDE ap-proaches can generate the background model to reduce ghostartifacts. However, these approaches sometimes fail to detectmoving objects because the luminance of the generated back-ground model cannot be easily updated in environments withsudden illumination change.

To compensate for the limitations of these approaches, an ap-proach must be developed to create an illumination-sensitivebackground model for object detection. To achieve this objec-tive, an object detection approach should be proposed using theglobal video trend along with the fast thresholding function.

III. PROPOSED METHOD

In order to solve the problem as mentioned above, we pro-pose a novel background subtraction approach, which includes abackground model module, an illumination evaluation module,and an object detection module. The flowchart of the proposedbackground subtraction approach is shown in Fig. 1.

1) Background model: In general, the background modelcan be generated by temporal average. However, temporalaverage cannot adapt to the current luminance of theincoming video frame . To solve this problem,the background model can be generated by the runningaverage expressed as follows:

(11)

where is the current background model,is the previous background model,

is the current video frame, and represents the adaptiveparameter. For the digital video sequence, the runningaverage can be simplified by a constant calculation asfollows:

if ,if .

(12)Note that is set to for initialization.


Fig. 1. Flowchart of the proposed background subtraction approach.

2) Illumination evaluation: According to entropy theory, adark image possesses a low entropy value due to insuf-ficient luminance of the image content, whereas a lightimage possesses a high entropy value because of suffi-cient luminance of the image content. Based on this char-acteristic, we use entropy formula to evaluate the changeof illumination. For each incoming video frame ,the probability density function can be expressed asfollows:

(13)

where represents each luminance of the video frame,represents the number of each luminance, and is theimage resolution. Based on , the entropy formula isexpressed as follows:

(14)

where is the current entropy value, is the min-imum luminance of the current video frame , and

is the maximum luminance of the current video frame.

After calculating the entropy value, the illuminationchange can be measured as follows:

if ,otherwise.

(15)

where represents the adjusted threshold which can beempirically set to 0.05. Note that equals one to indicatethe sudden illumination change. When equals one, thebackground candidates can be updated by the followingfunction:

if ,if .

(16)

where is the light background candidate, isthe dark background candidate, is the mean lu-minance of , is the mean luminance of

, and is the mean luminance of . The lightand dark background candidates can be used to updatethe luminance of the current background model. Whenequals one, the current background model can be updatedby the following function:

if ,otherwise,

(17)

where is the mean luminance of . Note that bothand are set to for initialization.

3) Object detection: After generating the illumination-sensi-tive background model, absolute difference canbe calculated between the incoming video frameand the background model as follows:

(18)

For each video frame , the binary mask of movingobjects can be generated as

if ,otherwise,

(19)

where represents the threshold value,equals 1 to represent a motion pixel and equals 0 to rep-resent a background pixel. When is regardedas a motion pixel, the threshold value can be calculated asfollows:

if ,if .

(20)

Note that is empirically set to twenty forinitialization.


Fig. 2. Various background models are generated by each approach in the (a)LB and (b) LS video sequences.

IV. EXPERIMENTAL RESULTS

This section summarizes the experimental results for motiondetection. Four motion detection approaches including the MTDapproach [10], the SSD approach, the MSDE approach [14],and the proposed illumination-sensitive background subtraction(ISBS) approach are compared using a selection of scenes fea-turing sudden illumination change.

A. Qualitative Measurement

In general, illumination change usually acts as sources ofcomplexity in environments. In order to test the performanceof each background subtraction approach, the “lobby (LB)”and “light switch (LS)” sequences are used in our experiments.Fig. 2 shows the LB and LS video sequences and the generatedbackground models obtained by the SSD approach, the MSDEapproach [14], and the ISBS approach, respectively. Accordingto Fig. 2, we can readily observe that sudden illuminationchanges occur in the original LB and LS sequences when lightsare switched on/off. As a result, all compared approaches failto generate an accurate background model when luminance issuddenly changed, with the only exception being the proposedISBS approach.

Fig. 3 shows the LB video sequence, ground truths, andbinary mask of moving objects obtained by the MTD [10],SSD, MSDE [14], and ISBS approaches, respectively. Since

Fig. 3. LB video sequence. (a) Four original LB frames and (b) ground truths.The remaining four sub-pictures present the binary mask of moving objects gen-erated by the (c) MTD [10], (d) SSD, (e) MSDE [14], (f), and ISBS approaches.

the MTD approach [10] doesn’t maintain a background modelbut holds several previous frames for motion detection, thebinary mask of moving objects can be generated at each frameas shown in Fig. 3(c). The SSD approach only calculates thetemporal average at each pixel for background generation,thus the generated background model cannot deal with theillumination change. However, the SSD approach calculates thetemporal standard deviation to determine the threshold value,which can be directly modified when the luminance changeincreases. As a result, Fig. 3(d) presents some comparablemotion masks, but noises still exist in some frames. The MSDEapproach [14] uses different time periods to generate severalreference images for smooth generation of the backgroundmodel. Unfortunately, this approach cannot adequately dealwith the illumination change, thus causing the serious motiondetection mistake shown in Fig. 3(e). Compared with otherapproaches, the proposed ISBS approach is more adaptable tothe sudden illumination change because of its determinationof light and dark background candidates, thus leading to thesuperior detection results shown in Fig. 3(f).

Fig. 4 shows the LS video sequence, ground truths, and bi-nary mask of moving objects obtained by the MTD [10], SSD,MSDE [14], and ISBS approaches, respectively. In this case,the MTD approach [10] retains some reference frames that ex-hibit lower or higher luminance than that of the current video


Fig. 4. LS video sequence. (a) Four original LS frames and (b) ground truths.The remaining four sub-pictures present the binary mask of moving objects gen-erated by the (c) MTD [10], (d) SSD, (e) MSDE [14], (f), and ISBS approaches.

frame. Therefore, this approach doesn’t work properly for mo-tion detection as shown in Fig. 4(c). Although the SSD approachcan generate the various threshold values through use of thetemporal standard deviation, the difference between the simplebackground model and incoming video frame is too large to dis-tribute the value of moving objects and background region dis-played in Fig. 4(d). Again, the MSDE approach [14] still fails togenerate the background model with illumination change, thusgenerating the inadequate detection results shown in Fig. 4(e).As indicated in Fig. 4(f), the ISBS approach presents a good de-tection result when compared with other approaches.

Fig. 5 shows the “intelligent room (IR)” video sequence,ground truths, and binary mask of moving objects obtainedby the MTD [10], SSD, MSDE [14], and ISBS approaches,respectively. According to Fig. 5(a), one person is working ina room with constant illumination. Due to the low quality ofdevice, some system noises are generated in this sequence. Asindicated in Fig. 5(c), the MTD approach generates a motionmask with artificial ghost trails because the previous referenceframes also involve the consecutive motion pixels of a movingobject. According to Fig. 5(d) and (e), both SSD and MSDE ap-proaches cannot deal with the system noises due to the inabilityof the threshold values of each pixel. Since the proposed ISBSapproach only modifies the threshold value through adaptionto moving objects, noise artifacts are reduced in the detectionresults shown in Fig. 5(f).

Fig. 5. IR video sequence. (a) Four original IR frames and (b) ground truths.The remaining four sub-pictures present the binary mask of moving objects gen-erated by the (c) MTD [10], (d) SSD, (e) MSDE [14], (f), and ISBS approaches.

Fig. 6(a) shows the original “moved object (MO)” videosequence and Fig. 6(b) shows its ground truths, while othersub-pictures show the binary mask of moving objects obtainedby the MTD [10], SSD, MSDE [14], and ISBS approaches,respectively. According to Fig. 6(a), one person enters theroom and sits in a chair to make a phone call. As displayed inFig. 6(c), the MTD approach generates the comparable motionmasks but artificial ghost trails are still involved. Accordingto Fig. 6(d) and (e), both SSD and MSDE approaches gen-erate undesirable motion masks that involve apparent noise.Moreover, the person sits down for a long time, thus creatinga misunderstanding between background and motion pixels atthe 985-th frame. Compared with the other approaches, the pro-posed ISBS approach is adaptive to environmental fluctuationsas shown in Fig. 6(f).

Fig. 7 shows the “airport (AP)” video sequence, groundtruths, and binary mask of moving objects obtained by theMTD [10], SSD, MSDE [14], and ISBS approaches, respec-tively. As indicated in Fig. 7(a), the sizes of moving objects aredifferent due to the depth of field. As displayed in Fig. 7(c), theMTD approach still generates artificial ghost trails in the de-tected motion masks. According to Fig. 7(d), the SSD approachcan generate the comparable detection results. In contrast,the MSDE approach is capable of adequately detecting eachmoving object but causes the serious mistakes indicated in


Fig. 6. MO video sequence. (a) Four original MO frames and (b) ground truths.The remaining four sub-pictures present the binary mask of moving objects gen-erated by the (c) MTD [10], (d) SSD, (e) MSDE [14], (f), and ISBS approaches.

Fig. 7(e). As shown in Fig. 7(f), the proposed ISBS approachgenerates once again the desirable detection results.

Fig. 8 shows the “shopping mall (SM)” video sequence,ground truths, and binary mask of moving objects obtainedby the MTD [10], SSD, MSDE [14], and ISBS approaches,respectively. Fig. 8(a) features many people shopping in a mall.With exception of the SSD and ISBS approaches, the use ofthe compared approaches still causes some problematic issues.Therefore, in addition to circumstances involving sudden illu-mination change, the proposed ISBS approach also works wellfor general situations.

B. Quantitative Measurement

In addition to qualitative assessment, quantitative measure-ment is also desirable in experiments. However, quantitativemeasurement of motion detection is not an easy task. In gen-eral, the accuracy of binary motion mask was usually assessedthrough use of and [7], [15]–[18]. The per-centage of true positives in the detected motion mask can beevaluated using the function as follows:

(21)

Fig. 7. AP video sequence. (a) Four original AP frames and (b) ground truths.The remaining four sub-pictures present the binary mask of moving objects gen-erated by the (c) MTD [10], (d) SSD, (e) MSDE [14], (f), and ISBS approaches.

where tp is the total number of true positive pixels in the mo-tion mask, fn is the total number of false negative pixels in themotion mask, and (tp fn) indicates the total number of true pos-itive pixels in the ground truth. On the other hand,presents the effect of false positives in the detected motion mask.The function can be expressed as

(22)

where fp is the total number of false positive pixels in the motionmask and (tp fp) indicates the total number of positive pixelsin the detected motion mask.

However, a motion mask with all positives can generate thehighest value and a motion mask with only one true pos-itive can generate the highest value. Thus, simplyusing and alone cannot supply an objectivemeasurement. To solve this problem, and canbe mixed to form the hybrid metrics: and [18].The and can be expressed as follows:

(23)

(24)


Fig. 8. SM video sequence. (a) Four original SM frames and (b) ground truths.The remaining four sub-pictures present the binary mask of moving objects gen-erated by the (c) MTD [10], (d) SSD, (e) MSDE [14], (f), and ISBS approaches.

Note that all metric-attained values range from 0 to 1, withhigher values representing better accuracy. Table I lists the av-erage , , , and of the detectionmasks generated by the MTD [10], SSD, MSDE [14], and pro-posed ISBS approaches for each tested video, respectively. Thisobservation demonstrates that the proposed ISBS approach al-most attains the highest accuracy among all the detection ap-proaches.

C. Time Complexity

In order to further demonstrate the efficiency of the proposedISBS approach, we evaluate the processing time of the motiondetection approaches for each video. Note that all approachesare implemented by prototype C programming language on alaptop with a Core 2 Duo CPU, a 4 GB RAM, and runningWindows 7 operation system.

Table II lists the frame per second (fps) of various approachesfor the LB, LS, IR, MO, AP, and SM sequences, respectively.While observation of Table II demonstrates that all approachescan be easily implemented for real-time application, the pro-posed ISBS approach has significantly lower computationalcomplexity than any of the other approaches.

TABLE IQUANTITATIVE MEASUREMENT FOR VARIOUS APPROACHES

TABLE IICOMPARISON OF FPS OF VARIOUS APPROACHES

V. CONCLUSION

This paper presents a novel background subtraction approachwhich features low computational complexity and high perfor-mance. Based on an illumination evaluation, the backgroundmodel can be easily updated with each change in luminancelevel by the determination of background candidates. The pro-posed approach also calculates the adaptive threshold values inorder to segment moving objects. Importantly, the proposed ap-proach can increase the accuracy of object detection and re-duce the processing time by constant calculation of the back-ground model. Experimental results indicate that the proposedapproach attains the most satisfactory outcome by both qualita-tive and quantitative evaluations compared with those of otherapproaches. Moreover, the analysis of time consumption alsoverifies that the proposed approach can be easily implementedin embedded systems with limited resources.

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Fan-Chieh Cheng received the B.S. degree inelectronic engineering from Huafan University,Taiwan (2005–2009). He is now a Ph.D. studentat Department of Electronic Engineering, NationalTaiwan University of Science and Technology inTaipei, Taiwan.

He has published and presented more than tenarticles in journals and at conferences. His researchinterests include digital image processing, videocoding, and bus codec design, in particular, con-trast enhancement, moving object detection, depth

generation, super-resolution, motion estimation, and vehicular CAN bustransmission.

Shih-Chia Huang received the master’s degree at theComputer Science Information Engineering Depart-ment of National Chiao Tung University in July 2005,and the doctoral degree at the Electrical EngineeringDepartment, National Taiwan University, Taiwan, inJanuary 2009.

He is an Assistant Professor at the Department ofElectronic Engineering of National Taipei Universityof Technology, Taiwan. He has published and pre-sented more than five papers in journals and at con-ferences, and also holds more than 24 U.S. and T.W.

patents. His research interests include image and video coding, video transmis-sion, video surveillance, error resilience and concealment techniques, digitalsignal processing, Java processor design, and embedded software and hardwareco-design.

Shanq-Jang Ruan (S’01–M’02) received theB.S. degree in computer science and informationengineering from Tamkang University, Taiwan(1992–1995), the M.S. degree in computer scienceand information engineering (1995–1997), andPh.D. degree in electrical engineering (1999–2002)from National Taiwan University, respectively.

He served as an Electronic officer in R.O.C.Air Force during July 1997 and May 1999. FromSeptember 2001 to May 2002, he served as a soft-ware engineer in Avant! Corporation. From June

2002 to November 2003, he joined Synopsys as a senior software engineer.His research interests include power efficient circuits and systems, imageprocessing, and electronic design automation. He was an assistant professorat Department of Electronic Engineering of National Taiwan University ofScience and Technology from 2003 to 2007. He is currently an Associate Pro-fessor at Department of Electronic Engineering of National Taiwan Universityof Science and Technology.

Illumination-Sensitive Background Modeling Approach for Accurate Moving Object Detection

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