Tracking individuals in surveillance video of a high-density crowd

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1 Trackig idividuals i surveillace video of a high-desity crowd Nighag Hu a,b, Heri Bouma a,*, Marcel Worrig b a TNO, P.O. Box 96864, 2509 JG The Hague, The Netherlads; b Uiversity of Amsterdam, P.O. Box 94323, 1098 GH Amsterdam, The Netherlads ABSTRACT Video cameras are widely used for moitorig public areas, such as trai statios, airports ad shoppig ceters. Whe crowds are dese, automatically trackig idividuals becomes a challegig task. We propose a ew tracker which employs a particle filter trackig framework, where the state trasitio model is estimated by a optical-flow algorithm. I this way, the state trasitio model directly uses the motio dyamics across the scee, which is better tha the traditioal way of a pre-defied dyamic model. Our result shows that the proposed tracker performs better o differet trackig challeges compared with the state-of-the-art trackers, while also improvig o the quality of the result. Keywords: Security, trackig, surveillace, image processig, crowd. 1. INTRODUCTION Video cameras are widely used i surveillace applicatios to moitor public areas, such as trai statios, airports ad shoppig ceters. Whe crowds are dese, automatically trackig idividuals becomes a challegig task. I this paper we propose a ew trackig techique to meet these challeges. Our tracker employs a particle filter trackig framework. Istead of usig a fixed pre-defied state trasitio model, we employ the optical-flow algorithm to estimate state trasitio. Sice optical-flow vectors are observed cues from the scee, they are more accurate tha the fixed models. I our method, the optical flow vectors are measured over the scee ad quatized withi local spatial-temporal regios. Optical flow vectors fallig i a regio are modeled ad the result is fed to the state trasitio model of particles withi the same regio. Some recet works require a log traiig period. But traiig over a log duratio of video may result i a less relevat model for trackig, especially for abormal motios. I our approach, o traiig process is required. The local regio of flow vectors is directly employed for state trasitio ad it oly requires a short part of the video prior to the frame uder cosideratio. To test the robustess of our tracker, i this paper, the performace is aalyzed over separate trackig challeges, such as ambiguous appearace, abormal pedestria behaviors, partial occlusio, ad differet desity of crowds. Our results show that the proposed tracker performs better o these challeges ad the performace is largely improved compared with the state-of-the-art trackers. 2. RELATED WORKS The defiitio of crowd varies largely over the literature. We defie four types of crowds. (1) Sparse crowd: oly a few pedestrias are observed i the scee. The pedestrias are defiitely ot gathered closely together ad less tha 50% of them are occluded [18][33]. (2) Moderate crowd: pedestrias cover over 50% area of the scee, ad about 50% - 80% of them are occluded [7][26]. (3) High desity crowd: pedestrias are observed i the whole area of the scee. Sice they are i small proximity, the movemets of pedestrias i the scee are physically costraied by the others earby. 80% - 100% of the people i the scee are occluded [15]. (4) Extremely dese crowd: cotais a extremely large umber of people, ad the resolutio o each perso is extremely low. All people are occluded, ad oly the head ad shoulders of the target ca be observed. 100% of the pedestrias i the scee are occluded [24]. To scope our target, i this paper, we focus o trackig i high desity crowds, where a large umber of people gather closely together, but the upper body of the pedestrias is still visible. Next, we will review differet approaches of trackig i the crowded scees. * heri.bouma@to.l; phoe ; Nighag Hu, Heri Bouma ad Marcel Worrig, Trackig idividuals i surveillace video of a high-desity crowd, Proc. SPIE, Vol. 8399, (2012); Copyright 2012 Society of Photo-Optical Istrumetatio Egieers (SPIE). Oe prit or electroic copy may be made for persoal use oly. Systematic reproductio ad distributio, duplicatio of ay material i this paper for a fee or for commercial purposes, or modificatio of the cotet of the paper are prohibited.

2 2.1 Trackig with frame-by-frame huma detectio I a scee with a sparse or moderate crowd, most pedestrias ca be fully observed ad the pedestrias ca be detected with a frame-based huma detector. Persos are the tracked by combiig detectios ito tracklets ad associatig the tracklets ito log trajectories [3][13]. Multiple persos are usually tracked at the same time to make the tracker more robust agaist occlusios. To fid the optimal associatio amog multiple tracklets, the Data Associatio based Trackig (DAT) algorithm [11][17][32] is applied. The DAT performs well i solvig the ambiguity problem ad reducig the rate of ID switches [19]. The associatio costs are measured by a weighted sum of differet cues, such as appearace (color histogram), motio, ad frame gap betwee two tracklets. The basic approach was exteded by usig shape ad appearace models [33], body-part detectors [29], or a boostig algorithm to trai the parameters [19]. However, i a deser crowd where the pedestrias are heavily occluded by the others, frame-based detectio is highly ustable, ad associatig amog a large umber of trajectories is very expesive. 2.2 Trackig with local feature poits Sice the local feature poits are large i quatity ad i geeral they are ulikely to all be affected by occlusios, the target ca be tracked by associatig local feature poits. Brostow e.a. [7] were amog the first to do this. They assume that the feature poits belogig to the same perso are close i space ad their motio exhibit high correlatio over time. A similar approach is proposed by Li et al. [18]. Sugimura et al. [26] make a extesio by employig gait features to separate pedestrias that are close i space. I a dese crowd, the gait features are ot oticeable. I high desity crowds, the local feature poits are frequetly occluded which results i a huge umber of short trackig fragmets. Associatig these fragmets with DAT ca be very challegig ad time cosumig. The other limitatio of such a approach is that targets movig together with the same speed caot be idetified as separate persos, whereas a local body movemet is usually wrogly estimated as a differet target. 2.3 Trackig with optical flow Optical flow algorithms have bee widely used for trackig pedestrias. Iitial approaches assume that optical flow o the target is uiform, ad the target is tracked by computig the mea flow aroud the target locatio [27][30]. Dema et al. [8] exteded this approach by usig foregroud-backgroud segmetatio to get a precise target regio. Such a approach is hardly possible i crowded scees. To deal with the high ambiguity of trackig i the frames, some approaches keep multiple hypotheses for the locatio of the target, ad they itegrate optical flow algorithms i a particle filter trackig framework [12]. This framework cosists of two compoets, measurig likelihood ad particle propagatio. Some papers [16][20][21] proposed to improve the likelihood measure by buildig a motio template of the target with flow vectors, while they choose a fixed dyamic model for particle propagatio. The fixed model makes the simplifyig assumptio that targets move with a costat speed i cosecutive frames. However, the simple pre-defied model does ot meet with the requiremets of trackig i crowded scees. I crowded scees, targets are frequetly occluded ad their regio chages over time. As a result, the target locatio does ot chage liearly. This requires a propagatio model that adaptively chages over time ad space with o-liear behavior. Rather tha buildig a dyamic model, Rodriguez [24] ad Kratz [15] icorporate optical flow algorithms i the particle propagatio. 2.4 Trackig with the motio of local areas I the high desity ad the extremely dese crowd situatios, the most promisig trackig algorithms use motio iformatio i local areas. Ali ad Shah [1] assume that pedestrias i the crowd behave i a similar way as particles i the flow. Their applicatio is limited to trackig pedestrias that move i a similar directio as the crowd. Rodriguez et al. [24] solve the trackig problem as i topic retrieval. They first divide the video ito short clips ad for each local area i the clip, flow vectors are quatized ito four categories based o the directio where vectors are headig. The quatized clips are traied withi the Correlated Topic Model (CTM), geeratig a set of topics. For each ew frame, a probability distributio over the topics is measured, ad the probability of the motio is the derived. The ew target positio is estimated as a combiatio of the observatio ad the tracker predictio. Similar to Rodriguez, Kratz e.a. [15] also divide the video ito spatial-temporal areas ad model the motios i each local space. But istead of coarsely quatizig the motio vectors ito four directios, they model the motio vectors of a local area with a Gaussia desity distributio. The temporal variatio of the motios is the leared by traiig a Hidde Markov Model (HMM) at each spatial area. With the HMMs, motios i the frame uder cosideratio ca be predicted. These approaches icorporate a traiig process that extracts motio statistics from the scee. For robust traiig, a log duratio of the traiig video is acquired. However, whe the duratio of traiig video icreases the frames are less relevat for trackig. Proc. SPIE, vol. 8399, , page 2 / 8

3 I this paper, we propose a ovel approach that tracks pedestrias with the previous observatio while disregardig the other previous frames. We model the local motios i the similar way as Kratz ad Nishio [15], but istead of learig the model with a log sequece of traiig data, we apply the local motios directly i particle propagatio. To the best of our kowledge, such a method has ot bee proposed before. Before we describe our ow approach, we study the particle filter i more detail. 3. COLOR-BASED PARTICLE FILTER TRACKING I a cluttered scee with a high desity crowd, the heavy occlusios ad the high ambiguity call for maitaiig multiple hypotheses while trackig. The particle filter [12] provides a robust way of solvig such problems. I this sectio, we will first itroduce the framework for the color-based particle filter tracker. The we will focus o measurig the two major compoets of the framework, the likelihood ad the state trasitio model. 3.1 Particle filter trackig framework Our goal is to track idividuals i a high desity crowd, i.e. fidig the most probable locatio S t (x t, y t ) of the perso give a sequece of observed frames [O 1,O 2,,O t ]. Typically, the particle filter makes use of a recursive Bayesia framework [12][15]: t 1: t t t t 1: P ( S O ) P( O S ) P( S S ) P( S O ) d( S ) (1) where P(S t O 1:t ) is the posterior probability, P(O t S t ) is the likelihood of the target ad P(S t S t-1 ) is the trasitio model from time t-1 to time t. The Bayesia framework follows a first-order Markov process, which meas the curret hypothetical locatio of the pedestria oly depeds o the previous state ad all the other past states or observatios are cosidered irrelevat. I the particle filter, the likelihood ad the state trasitio model are re-measured every iteratio. I the followig two subsectios, we itroduce the measuremet of the two compoets separately. 3.2 Likelihood The likelihood P(O t S t ) refers to the likelihood of the observatio O t beig the same as the target that we are trackig. The likelihood is typically measured iversely proportioal to the (e.g., Bhattacharyya) distace betwee two color histograms. RGB or HSV are two commoly used color spaces for the histograms, the first of which is sesitive to chages i illumiatio ad the latter may become ustable for colors with low value or saturatio. 3.3 State trasitio model The state trasitio model P(S t S t-1 ) is the other compoet i the particle filter, ad it determies how the particles are propagated i each iteratio. A traditioal state trasitio model assumes that the pedestria moves with a costat speed t i cosecutive frames ( S )' ( S )'. With such assumptio, we ca update the state S of the particle by: where is a costat for weightig, variable. S t S t 1 ( S )' (2) t 1 t 1 ( S )' is the speed of the target at time t-1, ad t is the Gaussia radom With the costat-velocity assumptio, the appearace likelihood of the target is the oly observed cue, thus the trackig is highly depedet o the performace of the likelihood measuremet. Due to the high ambiguity of the scee, measurig likelihood from the appearace is very hard. To make a more robust tracker for crowded scees, we itroduce a ovel approach, which uses the previous observatio as the state trasitio model i the particle filter. 4. TRACKING WITH PREVIOUS OBSERVATION I this sectio, we preset a ovel approach that employs the previous observatio as the trasitio model i the particle filter. Proc. SPIE, vol. 8399, , page 3 / 8

4 4.1 Capturig motio with optical flow The first step of our approach is to extract the motio by computig optical flow vectors. The optical flow algorithm measures the shift of pixels betwee two cosecutive frames. 4.2 Modelig observatio i local areas To extract the motio kowledge locally, the volume of motio vectors is subdivided ito local spatial-temporal areas, formig a set of cuboids with motio statistics. Kratz [15] proposed to compute the 3D gradiets of the itesity i the local area ad model them with 3D Gaussias. I our approach, however, we use the optical flow vectors directly, sice we cosider the flow vectors to be more stable tha the 3D gradiets. As the cuboid cotais flow vectors withi a short time spa ad a similarly small spatial area, we assume that the desity distributio of the vectors follows a 2D Gaussia distributio. 4.3 Embeddig previous observatio for trackig I the cotext of particle-filter trackig, the propagatio is drive by a state trasitio model, which accouts for the particles displacemet i the frames. I our paper, the state trasitio model is approximated by the previous observatio captured i the same spatial locatio as the particle. Assumig a particle is located i a cuboid defied by spatial regio R ad temporal duratio T, ad the particle is to be propagated from frame t-1 to frame t. The state trasitio model is approximated by the previous observatio O T-1 R. Formally, the particle state i the ext frame is propagated by: t T 1 S S O S (, ) (3) R By employig the previous observatio i particle propagatio, the two observed cues of trackig, i.e. the motio ad the appearace of the target, are fused properly i the particle filter. I such a way, targets ca be tracked with both observed cues at the same time ad the ambiguity of the appearace ca be reduced by addig the motios. 5.1 Dataset 5. EXPERIMENTS AND RESULTS The approach was evaluated o two crowded scees (Figure 1). The first scee cotais a high desity crowd i the music festival Love Parade (loveparade.de). The secod scee is recorded at the trai statio of Amsterdam. For the first scee, we evaluated our system with two separate sequeces of 300 frames (approximately 10 secods). The dimesio of a sigle frame is 1280x720 pixels ad i our experimet we oly used a sub-regio of 350x1030 pixels (average size of the pedestrias is 19x29) which was relevat for trackig. To aalyze the trackig performace, we maually selected 10 ormal pedestrias (movig with the crowd) ad 3 abormal pedestrias (ot movig with the crowd) for evaluatio. For the secod scee, a sequece of 300 frames was used with the dimesio of 480x640 pixels (average pedestria is 25x56). I this scee 10 pedestrias were selected as groud-truth targets. I the followig, we first itroduce the evaluatio criteria used i the paper. The we discuss the implemetatio of the differet trackig approaches separately. Fially, we evaluate the trackig algorithms ad compare the trackig results i both scees. Figure 1. Two frames from the sequeces with the music festival ad the trai statio. Proc. SPIE, vol. 8399, , page 4 / 8

5 5.2 Evaluatio criteria The related works [1][15][24] use the average distace error as the evaluatio criteria. The drawback of such measuremet is that it does ot reflect how well the tracker ca focus o the target. I this paper, we decompose the evaluatio ito two parts. First, we determie whether targets are tracked correctly based o overlap, which is our primary criterio. Oly whe the target is tracked correctly, we measure the average distace error of the track as a measure of accuracy. 5.3 Trackig with color-based particle filter I this paper, we employ as a baselie the traditioal color-based particle filter [23], which assumes the target moves with a costat speed i cosecutive frames. Based o experimets [10], we chose the parameter to weight the target s speed i the previous frame to be =1 ad the variace of the radom compoet. 5.4 Trackig with motio patters We also implemeted the state-of-the-art approach by Kratz ad Nishio [15] for compariso. We follow their approach to model the local motios with a Gaussia desity distributio i each spatial-temporal area. The temporal motio dyamics are leared by traiig a Hidde Markov Model (HMM). I the HMM, the states are geerated by applyig a o-lie clusterig algorithm over the Gaussia desity distributios, where the Kullback-Leibler (KL) divergece is applied as the distace measuremet betwee the cluster ad the Gaussias. Here the states are cosidered as motio patters [14]. Sice they also employ the particle filter as their trackig framework, i the followig, we deote their approach as PF+MP+HMM for the coveiece of illustratio. With a small umber of frames, the HMMs caot be traied sufficietly, while with a large umber of frames, it becomes less relevat for trackig. Based o experimets [10] a duratio of 300 frames was chose. 5.5 Trackig with the previous observatio. Istead of modelig the HMMs, our proposed tracker, PF+PO, uses the previous observatio directly as the state trasitio model i the particle filter. The PO is a 2-dimesioal Gaussia desity distributio, compactly represetig the optical flow vectors i a local temporal-spatial area. The optical-flow vectors are computed with the Guar Farebäck s algorithm [9] i OpeCV [6]. A set of trajectories geerated by our proposed trackig algorithm is show i Figure 13. The proposed tracker is evaluated with varyig duratios of the previous observatio. The PF+PO tracker appears to perform optimally whe the PO cotais betwee 1 to 10 frames [10] ad we use 10 as the optimal duratio for compariso. 5.6 Evaluatio ad compariso To compare the baselie particle filter (PF), state-of-the-art motio patters (PF+MP+HMM) ad the ovel previousobservatio (PF+PO), we applied each approach to the two scees. To reduce the radomess, each pedestria is tracked for 10 iteratios. A few examples are show i Figure 2. Figure 2. By exploitig the previous observatio i the local area, most of the pedestrias are tracked as they moved through a high desity crowd. Proc. SPIE, vol. 8399, , page 5 / 8

6 percetage of pedestrias Figure 3 compares the trackig results over time. The result shows that PF+PO is able to track the pedestrias for loger time tha the others. We observed that the trackig result of PF is highly depedet o the appearaces of the pedestrias. Figure 4 shows that the bad performig targets of PF, which exhibit either low saturatio or low value i the HSV space, fail due to a ustable hue compoet. Table 1 shows the quatitative results i both scees. Beefitig from the costat speed assumptio, the baselie tracker PF achieves the best performace whe trackig with full occlusios (especially i the first scee). By modelig the motios with HMM, the PF+MP+HMM tracker scores higher for partial occlusios ad ormal pedestrias tha PF i the first scee, but still lower tha PF+PO because the motio statistics i the HMM are more relevat for the traiig video tha the frame of trackig. Whe trackig the abormal pedestrias, the performace of PF+MP+HMM is eve worse tha our baselie tracker. This is because the abormal motio is ot modeled i the HMM, thus the HMM caot assig good predictios to the abormal pedestrias. I the secod scee, the PF+MP+HMM tracker completely fails, because the trackig algorithm caot hadle the areas with hardly ay motio. Our proposed tracker PF+PO scores the highest o partial occlusios, abormal behaviors, ormal behaviors, ad o average. Particularly, whe trackig the abormal pedestrias i the first scee, the performace is improved by 30% compared with the other trackers percetage of successful trackig frames (time) Figure 3. The percetage pedestrias that is successfully tracked for a umber of frames for PF (red; dash-dot), PF+MP+HMM (blue; dashed) ad PF+PO (black; solid). Figure 4. The graph shows the trackig performace of PF over pedestrias, where the color represets the appearace of the target ad the size its performace. The PF shows bad performace for low saturatio ad low value. Proc. SPIE, vol. 8399, , page 6 / 8

7 Table 3. Trackig results of differet approaches i the two scees. The trackig performace is the average ratio of persos tracked over the complete duratio. Dataset Method Partial Occl. Full Occl. Abormal Normal Average Number of Targets Musical PF Festival PF+MP+HMM PF+PO Number of Trai Statio Targets PF PF+MP+HMM PF+PO CONCLUSIONS I this paper, we proposed a method that uses the previous observatio (PO) to track idividuals i high-desity crowds. The proposed tracker employs a particle-filter trackig framework, where the particles are propagated accordig to the previous observatios. These observatios are defied as a two-dimesioal Gaussia, which models the dese distributio of the optical flow vectors i a local spatial-temporal area. Accordig to our experimets, the traditioal color-based particle filter (PF) fails maily o the targets with black, gray ad white appearaces, due to a ustable hue compoet. The result shows that modelig the temporal variatio of motios with HMMs may ot be a suitable choice for trackig i our video data. To trai the HMMs properly, a large size of the traiig data is preferred. However, the traiig data becomes less relevat for trackig as the size icreases. The results also show that choosig a optimal legth i betwee helps to improve the trackig performace o ormal pedestrias. For the abormal persos, however, the performace is eve worse tha the PF tracker. Besides, the HMM tracker is very sesitive to regios with hardly ay motio. By evaluatig the proposed (PF+PO) tracker o varyig duratio of the previous observatio, we fid that better trackig results ca be achieved by usig a small umber of frames i the previous observatio. REFERENCES [1] Ali, S. ad Shah, M., Floor fields for trackig i high desity crowd scees, ECCV II, 1 14 (2008). [2] Barro, J.L, Fleet, D.J, ad Beauchemi, S.S., Performace of optical flow techiques, IJCV 12(1), (1994). [3] Berclaz, J., Fleuret, F. ad Fua, P., Robust people trackig with global trajectory optimizatio, IEEE CVPR 1, (2006). [4] Bouma, H., Borsboom, S., Hollader, R., Ladsmeer, S., Worrig, M., Re-idetificatio of persos i multicamera surveillace uder varyig viewpoits ad illumiatio, Proc. SPIE 8359, (2012). [5] Bouma, H., Hackma, P., Marck, J.-W., Peig, L., Hollader, R., Hove, J.M., Broek, S.P. va de, Schutte, K., Burghouts, G.J., Automatic huma actio recogitio i a scee from visual iputs, Proc. SPIE 8388, (2012). [6] Bradski, G. ad Kaehler, A., [Learig OpeCV: Computer visio with the OpeCV library], O Reilly Media, USA, (2008). [7] Brostow, G.J. ad Cipolla, R., Usupervised Bayesia detectio of idepedet motio i crowds, IEEE CVPR 1, (2006). [8] Dema, S. Chadra, V. ad Sridhara S., Adaptive optical flow for perso trackig, IEEE Digital Image Computig: Techiques ad Applicatios, (2005). Proc. SPIE, vol. 8399, , page 7 / 8

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