Pedestrian Motion Tracking and Crowd Abnormal Behavior Detection Based on Intelligent Video Surveillance

Transcription

1 2598 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER 2014 Pedesrian Moion Tracing and Crowd Abnormal Behavior Deecion Based on Inelligen Video Surveillance Fan Zhao School of Compuer Science and Informaion Engineering, Zhejiang Gongshang Universiy, Hangzhou, China Jin Li* School of Compuer Science and Informaion Engineering, Zhejiang Gongshang Universiy, Hangzhou, China; Conemporary Business and Trade Research Cener of Zhejiang Gongshang Universiy, Hangzhou, China *Corresponding auhor: Absrac Pedesrian racing and deecion of crowd abnormal aciviy under dynamic and complex bacground using Inelligen Video Surveillance (IVS) sysem are beneficial for securiy in public places. This paper presens a pedesrian racing mehod combing Hisogram of Oriened Gradiens (HOG) deecion and paricle filer. This mehod regards he paricle filer as he racing framewor, idenifies he arge area according o he resul of HOG deecion and modifies paricle sampling consanly. Our mehod can rac pedesrians in dynamic bacgrounds more accuraely compared wih he radiional paricle filer algorihms. Meanwhile, a mehod o deec crowd abnormal aciviy is also proposed based on a model of crowd feaures using Mixure of Gaussian (MOG). This mehod calculaes feaures of crowd-ineres poins, hen esablishes he crowd feaures model using MOG, conducs self-adapive updaing and deecs abnormal aciviy by maching he inpu feaure wih model disribuion. Experimens show our algorihm can efficienly deec abnormal velociy and escape panic in crowds wih a high deecion rae and a relaively low false alarm rae. Index Terms Pedesrian Tracing; Behavior Deecion; Inelligen Video Surveillance; Abnormal Aciviy I. INTRODUCTION Inelligen Video Surveillance (IVS) is a synheic applicaion of a variey of sciences and echnologies relaed o compuer vision. I employs image processing, paern recogniion, arificial inelligence and oher echnologies o process and analyze he video image sequences capured by monioring sysem, inelligenly undersands video conen and maes quic response. Generally, wha inelligen video surveillance concerns and sudies is he moving objec in video. By means of deecing, idenifying, racing, comprehending and ec., feaures such as color, shape, conour and gradien are exraced. Aiude, velociy, rajecory and oher movemens are recorded o help researchers idenify arge class, comprehend arge behavior and herefore mae judgmen, early warning, managemen and oher relaed decisions correcly. Wih he widespread applicaions of IVS echnology in numerous fields of human sociey, i has become one of he core requiremens concerning human deecion and behavior analysis. Afer 9 11 aacs, every counry in he world has a new insigh of inernaional errorism and domesic securiy siuaion, and srenghens he monioring managemen of densely crowd places. More and more video surveillance sysems aiming a pedesrians emerge in airpors, saions, pors, bans, squares and some oher imporan places. Crowd conrol and pedesrian securiy problem in public places have been a grea challenge. By 2008, here are approximaely 2 million cameras used for ciy surveillance and alarm sysem in China, and by 2011, Naional Urban Alarm and Monioring Sysem Consrucion Pilo Projec (3111 pilo projec) has covered every prefecure-level ciy of all he provinces, municipaliies and auonomous regions naionwide. As shown in China Securiy Indusry Twelfh Five-Year ( ) Developmen Plan, he oupu value of China's video surveillance sysem will exceed 100 billion Yuan by 2015, accouning for more han 55% of securiy elecronic producs. Ye in hese projecs and programs, dynamic deecion, dynamic early warning, inelligen analysis and processing of video image, biomerics idenificaion and oher major conen are inseparable from he relaed researches of pedesrian moion deecion and analysis. Recen several years has winessed enormous amoun of effors invesed o he research of inelligen video surveillance area from academia o indusry, and many pracical resuls have already been achieved. In 1997, he U.S. Defense Advanced Research Projecs Agency (DARPA) esablished Video Surveillance and Monioring (VSAM) which was led by Carnegie Mellon Universiy and joinly paricipaed by Massachuses Insiue of Technology and many oher higher educaion insiuions and research insiuions, wha were mainly sudied was he video undersanding echnology of real-ime doi: /jnw

2 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER auomaic monioring of miliary and civilian scenes. Hariaoglu e al. [1] developed a subsysem W 4 (Who are hey? When do hey ac? Where do hey ac? Wha are hey doing?) of VSAM, which used single camera and he capured grayscale image o locae and segmen pedesrians in complex oudoor environmen and implemened real-ime racing of several pedesrians. VSAM-based muli-sensor echnology was proposed by Collins e al. [2] o develop he monioring sysem applicable for campus. Some maure sysems have also been widely used in erms of civilian research. Wren e al. [3] developed a Pfinder (person finder) sysem suiable for single non-occluded pedesrian and fixed camera case, allowing real-ime deecion and racing of pedesrian under complex environmen. Lipon e al. [4] sudied a sysem using newor conneced o muliple cameras, which achieved deecion and racing of muliple pedesrians and vehicles in a large scale range and monioring heir aciviies over a relaively long period of ime. In 2005, several European organizaions joinly developed he ISCAPS (Inegraed Surveillance of Crowded Areas for Public Securiy) projecs, which primarily sudied human auo-monioring echnology used for discovering poenial securiy hrea in dense region of crowd. In recen years, researches ha can be applied o he analysis of human behavior mainly focused on he deecion, racing, recogniion of objec and a higher level of behavior analysis. Coninuous improvemen in deecion accuracy, robusness and rapidiy has been regarded as major direcion, herefore a number of new soluions and algorihms arise. For arge racing, he primary mehods included hose focusing on local objec feaure [5] [6], approaches of esablishing racing model [7-9] and hose based on acive conour [10] [11], he emphasis of hese lieraures involved muli-objecive, camera moion and oher complex issues. On he basis of radiional use of Kalman filer, he mehod adoping paricle filer [12] is now in rapid developmen. In erms of pedesrian idenificaion, he research program concenraed on using paern recogniion mehod for classificaion, and research conen mainly consis of feaure exracion and classifier consrucion. Mehods based on moion characerisics [13] [14] and shape propery [15] [16] were applied wih regard o feaure exracion, moion characerisics primarily referred o he specific rigidiy and periodiciy of human moion, while shape propery can be comprised of region dispersion of image, aspec raio, gradien and many oher characerisics. As for he consrucion of classifier, mehods include SVM and Boos are commonly adoped in research wih he inen of shorening raining ime and improving classificaion rae during deecion process. Some algorihms for human behavior recogniion, crowd behavior analysis and abnormal deecion have also gained exensive aenion over he pas few years [17-22]. Assheon and Huner [23] presened he mixure of uniform and Gaussian Hough Transform for shapebased objec deecion and racing, proposed a varian of he generalized Hough ransform. Liu, Chang and Guo [24] proposed a probabiliy-based pedesrian mas prefilering o filer ou non-pedesrian regions meanwhile reaining mos of he real pedesrians. Paricle filering mehod has proven o be useful in dealing wih non-linear, non-gaussian sysems, herefore, i can be applied o he handling of complex dynamic scenarios during arge racing process. In recen years, racing algorihm which employs paricle filer mehod coninues o develop, however, argeed processing under complex siuaions such as camera lens moving and pedesrian scale change is sill difficul o achieve when dealing wih arge pedesrian in video, which may lead o racing errors and even loss of arge. In order o improve racing resul of video image-based pedesrian racing, we adop he srucure of basic paricle filering mehod o propose a mehod of pedesrian moion racing which inegraes paricle filer wih HOG feaure deecion. HOG provides an explici descripion of he shape of local objec in he image depending on he saisics of hisogram disribuions in gradien orienaion, which exhibis significan effec on he classificaion of arge pedesrians. Humans are social animals and urban environmen is he disribuion cener of human. Therefore, capuring crowd in usual ciy video surveillance sysems is a quie common phenomenon. While crowd behavior, especially abnormal behavior usually conains some imporan informaion required in monioring and early warning. As a consequence, deecion of abnormal crowd behavior has become he objec ha inelligen video surveillance sysem focuses on and sudies as well as a research hospo in pedesrian moion analysis in recen years. Hence, we shall sudy he deecion of abnormal aciviy in crowds, by exracing crowd feaure, esimae moion parameers of crowd-ineres poins via bloc maching mehod, and hen employ moion parameers analysis mehod o deec crowd gahering, dispersing, sranding, running and oher group behaviors. II. PEDESTRIAN TRACKING INTEGRATING HOG DETECTION WITH PARTICLE FILTER During he process of pedesrian racing, he proposed mehod firsly esablishes a dynamic model which describes he arge locaion and hen adops paricle filer algorihm o conduc pedesrian racing. Afer obaining he region of arge pedesrian, we use he mehod of HOG feaure deecion o implemen classificaion of pedesrians in he specified region, and modify he posiion of he raced objec according o he resul of pedesrian deecion, hus improve he sampling of paricle filer o reduce he racing error. A. Tracing Model The posiion of arge pedesrian is defined using a recangular deecion window, he sae vecor is described as X [ x, y, w, h], where ( xy, ) is in he cener of he deecion window, wh, are widh and heigh of window respecively. Based on he second-order auoregressive model, he sysem is described as follows:

3 2600 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER 2014 Iniial frame Frame11 Frame129 (a) Tracing resuls of he radiional paricle filer mehod Iniial frame Frame3 Frame117 (b) Tracing resuls of our algorihm Figure 1. Comparisons of racing resuls under dynamic bacground Iniial frame Frame99 Frame205 Frame260 Frame300 Frame491 Figure 2. Pedesrian racing process under camera movemen X A ( X X ) A ( X X ) (1) where, is a zero-mean Gaussian random process vecor whose variance vecor is adjused appropriaely according o scenarios. The scale and roaion invariance of he color characerisic of an objec maes i suiable o be used in racing process. Hence, our algorihm adops color hisogram disribuion as he observaion model and performs in HSV (hue, sauraion, value) space. Taing 10 segmens quanified on hree componens H, S, V respecively, he hisogram is consequenly divided ino segmens M=1000. Le p be he arge model, q( x, y ) denoes he color hisogram of he ( xy, ) cenered deecion region, l( p, q( x, y )) and d( p, q( x, y )) are similariy and disance beween he wo respecively. According o Bhaacharyya coefficien, we have: M l( p, q( x, y)) h ( i), h ( i) (2) i1 p q( x, y) d( p, q( x, y)) 1 l( p, q( x, y)) (3) where hp () i, h () q( x, y) i are heir respecive color hisogram componen. When he posiion of objec in ime 1 is obained hrough observaion and esimaion, expand a cerain range around from his posiion (he scale of he exended area can be prese according o experiences), HOG pedesrian deecion can be conduced in his relaively larger region. The region of pedesrians is decided via HOG classifier, and is credibiliy is calculaed based on

4 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER he posiion informaion and color hisogram. Selec he region wih he maximum credibiliy and exceeding he hreshold l as a new emplae, re-ener paricle filer iniializaion sep and coninue racing process. B. Algorihm Seps The proposed racing algorihm in his paper is inegraed wih HOG deecion on he basis of general paricle filer algorihm flow. The seps can be described as follows: Sep 1. Iniializaion. Esablish he model when 0, exrac color hisogram from he argeed emplae, hen i exrac N iniial paricles { X0,1/ N, i 1,2,, N} from prior disribuion; Sep2. Paricle predicion. Based on he paricle swarm { X i 1,1/ N, i 1,2,, N} in ime 1, according o he second-order auoregressive dynamic model, we can i i obain paricle swarm { X,, i 1, 2,, N} in ime ; Sep3. Paricle observaion. Use color hisogram o calculae observaion lielihood for each paricle, hus we have { L i, i 1,2,, N} ; Sep4. Updae he paricle weigh. Afer having he observaion lielihood normalized, updae he paricle i i j weigh o be L / L, i 1,2,, N ; N j1 Sep5. HOG pedesrian deecion. Calculae he probabiliy afer esimaion, he obained posiion is X N i i i X i1, expand he region m X by (defaul value) imes, and hen conduc HOG pedesrian deecion in his expanded region. If here is no pedesrian arge in classificaion resul, coninue o he nex sep; If here are P regions belonging o pedesrians, calculae heir credibiliy {, m m 1,2,, P }, selec he region wih he maximum credibiliy exceeding he hreshold l as is new emplae, hen reurn o he iniializaion sep; Sep6. Resample. To solve paricle lacing problem and reain paricles wih large weigh, exclude paricles whose weigh is less han hreshold and fill he new paricle swarm { X i 1,1/ N, i 1,2,, N}, hen go o he paricle predicion sep. C. Experimenmal Resuls for Tracing Algorihm In order o verify he racing effec of our algorihm, we carry ou pedesrian racing experimen in video sequences. The configuraion of he running compuer is Penium Dual Core (2.20GHz CPU) and 2G Mb ROM. Experimenal parameers are se: dynamic model parameers A1 2, A2 1, credibiliy hreshold l 0.5, paricle number N 100. We performed experimen on he videos wih dynamic bacground exising dynamic panning and zooming changes, he raced objec is among a pluraliy of pedesrians and varies in size aribued o he difference in disance in video sequence. Compared wih general paricle filer algorihm he experimenal resuls are l shown in Figure 1. We can see from he comparison: our algorihm ouperforms he general paricle filer racing algorihm in racing resuls wih less racing error in dynamic video, i offers a beer adapabiliy o bacground and arge scale changes wih he inegraion of HOG deecion which can modify paricle sampling during he racing process We furher carry ou racing experimen for pedesrians in more complex and versaile video image sequences, he resuls are shown in Figure 2. During he process of video capure, here exiss simple movemen of lens srech and direcional movemen in camera and significan changes in he size of raced pedesrians. Sequenial racing of pedesrians in his video canno be achieved using common daa-driven algorihm and radiional paricle filer mehods; however, our algorihm implemens comparaively complee racing which can handle hese complex siuaions more effecively. III. CROWD ABNORMAL DETECTION The sudy of curren crowd behavior analysis can be divided ino wo caegories according o he difference of he arge concerned: individual-based analysis and enirey-based analysis. Individual-based analysis aims a single pedesrian arge, which recognizes individual behavior paern hrough analysis and furhermore analyzes abnormal behavior afer obaining crowd moion informaion. However, in cases of comparaively dense crowd, segmenaion and racing of individuals will become difficul owing o he severe occlusion in pedesrians. Hence, he enirey-based analysis which exracs crowd characerisic parameers o deec abnormal aciviy has araced considerable aenion. In his paper, we presen an algorihm i.e. analysis and modeling of he feaure parameer of crowd ineres poin. Firs, exrac POI (Poins of Ineres) in crowd wihin he moniored region, hen analyze he saisical eigenvalues such as number of POI, densiy, velociy and direcion of movemen, exrac hese feaures o build he Gaussian mixure model of crowd eigenvalues o describe crowd behavior and perform updaing and mainenance. As o he deecion of crowd behavior, we mach he POI feaure exraced from he inpu image each ime wih he Gaussian mixure model esablished afer a period of raining, abnormal even is considered o have been deeced if here is a mismach. A. Gaussian Mixure Bacground Model Gaussian mixure bacground model can well describe he feaure disribuion of pixel when operaing bacground modeling and objec segmenaion on video image sequence. In his secion, we apply his model o he esablishmen of he descripion model of group feaure in crowd video image sequence. Se X o be he color value of cerain pixel, hen i can be approximaed by he weighed sum of several Gaussian disribuions, suppose ha a serials of hisorical daa se of X in ime is expressed as { X1. X 2,, X }, hus he probabiliy of curren X is defined as follows:

5 2602 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER 2014 K P( X ) ( X,, ) (4) i, i, i, i1 where K denoes he number of normal disribuions,,, are weigh, mean and covariance of he i h i, i, i, Gaussian disribuion in ime, respecively. describes he probabiliy densiy funcion of he i h Gaussian disribuion, i is given by: ( X,, ) 1 n (2 ) e 1 ( ) T 1 X ( X ) 2 Use online K-means approximaion mehod o esimae he parameers of X disribuion, he weigh is updaed as follows:,, 1, (5) (1 ) M (6) where is learning rae. If curren X is locaed in a ripling sandard deviaion range of cerain normal disribuion, we consider ha a mach occurs, oherwise i is deemed as unmached. The mean and variance remain unchanged for each unmached normal disribuion, while for hose mached hey are updaed as below: (1 ) X (7) 1 (1 ) ( X ) ( X ) (8) 2 2 T 1 (, ) (9) X Afer obaining he parameers of each curren normal disribuion, we deermine which normal disribuion he curren variable value belongs o. The self-adapive bacground model is ulimaely comprised by he mixure of he firs B disribuions which possess relaively large weigh and small volailiy, i can be obained as follows: B arg minb b T (10) 1 where T is a hreshold. Then, we deermine wheher he curren pixel values mach he mixure of normal disribuion of bacground or no, hose unmached are regarded as foreground objec. B. Abnormal Deecion Process We characerize he disribuion condiion of crowd feaure via Gaussian mixure model, deec feaures which are unmached wih he esablished group feaure model by he way of bacground segmenaion, hus deec anomalous even occurred in crowd. In he following secion, we will ae he analysis of velociy feaure anomaly of some ROI (Region of Ineres) as an example o discuss he esablishmen and updae mehod of feaure model. Le V be he crowd velociy feaure of cerain region in he image, describe he probabiliy disribuion of V in cerain cell region of video image using he mixure of K normal disribuion, if V is characerized as he mixure of hree ind of movemen paerns: high, medium and low, he value of K is aen as 3. Tae he following seps o esablish and updae he model of crowd velociy parameer V : Sep 1. Iniializaion Assume ha velociy feaure is uniformly disribued in [0,1], divide [0,1] ino hree segmens o describe he range of hree inds of movemen paerns: low, medium and high. Now we have K =3, hus he expecaion of hree paerns falls on he cener posiion of hese 3 segmens, he variance of paern is iniialized o and weigh is iniialized as Sep 2. Maching of feaure disribuion For a frame of newly inpu image, use he feaure parameers V in ROI o mach he 3 disribuions in Gaussian mixure model sequenially. If i saisfies VROI [ 2.5, 2.5 ] wih regard o one of hese disribuions, hen we consider ha he inpu feaure is mached wih he curren disribuion, vice versa. Sep 3. Model updae Updae he parameer of mached disribuion according o he following equaions:, 1 (1 ), V (11) (1 ) ( V ) ( V ) (12) 2 2 T, 1,, 1, 1 where, and, denoe mean and variance of he h disribuion, respecively. The learning rae of his model is /,, is he weigh learning rae, and, is described as he weigh, indicaing he probabiliy of he occurrence of he h disribuion. The weigh can be updaed below:, (1 ), M, 1 (13) where M, 1 aes he value of 1 if a mach occurs, oherwise i is 0. Sep 4. Abnormal deecion Sor he disribuions in descending order according o /. Wih he proceeding of model raining process, disribuions wih frequen occurrences and relaively small variance will be placed o he fron of he queue afer several imes of soring. We consider he eigenvalues which frequenly occur as a represenaion of normal even, analogically we can hin of i as he bacground in he image; while anomalous even, which corresponds o he par ha does no mach he model, is regarded as he foreground analogically. Hence, a mehod of bacground segmenaion of Gaussian mixure model can be employed o deec anomalous even, ha is, if he currenly inpu feaure is mached wih one of he firs B disribuions, we consider i is normal condiion in curren ROI, oherwise, an excepion is considered o have been deeced. Here, B is defined as follows: b, TB (14) 1 Barg min( ) b

6 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER (a) An insan of dispersed crowd running (b) Deecion Schemaic Figure 3. Tesing resul of sudden dispersed running crowd TABLE I. COMPARISONS OF THE DETECTION RESULTS FOR THE THREE ABNORMAL DETECTION ALGORITHMS Deecion rae False alarm rae MDT algorihm 75% 25% SF algorihm 80% 19% The proposed Algorihm 81% 2% where T B is a prese hreshold, he bigger T B is, he smaller he chances ha deeced abnormal condiion occurs in hisory are. Similarly, a variey of anomalous evens occur in crowd scenarios can be deeced. The modeling analysis of POI feaures is suggesed in order o analyze anomalous even in ROI. Firs, selec he POI of he characerized crowd in ROI, nex exrac quaniy, velociy, direcion and oher saisical characerisics of POI, and hen uilize he Gaussian mixure model which esablished muliple feaure of he crowd. When operaing bacground segmenaion hrough model raining and updaing, he inpu feaure which maches feaures such as quaniy, velociy and movemen direcion of POI belongs o he bacground disribuion; and he unmached inpu feaure wih small occurring probabiliy hus belongs o he foreground, indicaing he occurrence of abnormal condiion. Abnormal moion of crowd can be deeced afer he segmenaion of hese foregrounds. C. Experimenal Resuls and Analysis Experimen is conduced on our algorihm using VC and OpenCV2.0 plaform and algorihm performance is esed as well. The configuraion of he running compuer is Penium Dual Core (2.20GHz CPU) and 2G Mb memories. The video daa se used in ess consiss of wo pars, namely, UCSD Library (Unusual crowd aciviy daase of Universiy of Minnesoa; UCSD anomaly deecion daase) and video segmens colleced in campus road circumsances during and afer class ime period. In he experimen, we employ our algorihm o conduc anomaly deecion o each frame in he video sequence of crowd moion and figure ou he number of frames where an anomaly is deeced, when he number of abnormal frames in a video sequence exceeds he prese hreshold, anomalous even in crowd is considered o have occurred in his video segmen. Figure 3 shows he condiion of algorihm deecion when here exiss crowd anomaly in video, he crowd is waling randomly a firs, and soon aferwards fleeing phenomenon occurs suddenly. As is shown in char (a), we inercep a frame of deecion resul image in he process of dispersed crowd running, he recangular box is he ineres deecion region se by users and he inside area of recangle is li up (covered wih a red mas ) aribued o he deecion of anomalies; char (b) describes he sae curve ploed hrough calculaion during he deecion process of algorihm, where he curve below shows he saus in esing process, he normal sae poin is indicaed by a small do while he abnormal sae poin is represened by a large do, herefore, he laer par which is hicer refers o a series of abnormal sae poins having been deeced, i means abnormal evens occur when he number of poins exceeds he prese hreshold. We have examined several videos which involve anomaly even in crowd. As is shown in Figure 4, images in column (a) describe normal crowd moion before he occurrence of abnormal condiion; column (b) corresponds o a frame of abnormal even occurring subsequenly in video. The sequence of esing video is from he op down and abnormal condiions include sudden crowd dispersal, swif passing of rider and unexpeced curved running. The recangles in figures refer o he ineres deecion region ha users selec o se, when here is an anomaly wih crowd deecion parameers in deecion box, he algorihm will ligh up he inside area of deecion box (filled wih a red mas). We record he resuls of abnormal deecion in hese hree esing videos and compare he resuls of our algorihm wih SF algorihm [25] and MDT algorihm [26], he resuls are shown in able 1. In he accuracy aspec of deecion resuls, our abnormal deecion model algorihm reaches 81% in average deecion rae wih he average faul alarm rae as low as 2%, ha is, we achieve relaively high deecion rae while eeping he faul alarm rae comparaively low. And in view of he execuion speed of algorihm, he proposed mehod can basically deec 5 frames of images wih a scale of per second. However, MDT algorihm is low in execuion speed wih he ime required o deec an even smaller image up o 20 seconds.

7 2604 JOURNAL OF NETWORKS, VOL. 9, NO. 10, OCTOBER 2014 (a) Normal movemen crowd (b) Crowd abnormal deecion Figure 4. Resuls of crowd abnormal deecion IV. CONCLUSION In his paper, we sudy he pedesrian racing and deecion of abnormal crowd behaviors. For he pedesrian racing, we adop he model-driven idea, considering he unsuiabiliy of general racing algorihm in cases of pedesrian arge scale change and camera moion, paricle filer framewor is adoped o esablish he observaion model via color hisogram and implemen sampling correcion using HOG deecion. Our algorihm aes advanage of he insensiiviy of color hisogram feaure o arge scale change and parial occlusion and uilizes he subsanially high deecion accuracy rae of pedesrian under dynamic bacground in HOG algorihm. The experimenal resuls show ha our algorihm achieves pedesrian arge racing under he video condiion of dynamic bacground and camera moion wih less racing error compared o convenional paricle filer algorihm, moreover, i exhibis significan racing effec on complex videos wih camera moion and pedesrian scale change which canno be successfully raced using radiional mehod. For he deecion of abnormal evens in crowd, we presen he following mehod in his paper: analyze he parameers of feaure poin from an overall perspecive, esablish crowd feaure Gaussian mixure model and perform self-adapive updaing, and deec abnormal even in crowds by he maching operaion beween he inpu feaure and model bacground disribuion. Segmenaion and racing of individual objec is dispensable in our algorihm and he raining of Gaussian mixure model is relaively simple and swif, hus enabling quic and efficien feaure exracion and anomaly deecion of crowd. As experimenal resuls demonsrae, abnormal phenomena in crowd such as fleeing and speed jump can be deeced in a relaively low false alarm rae by he proposed mehod, moreover, our algorihm ouperforms SF and MDT algorihm wih higher deecion accuracy and faser velociy. ACKNOWLEDGMENT The auhors wish o han he reviewers for heir valuable commens. This wor was suppored in par by he Zhejiang Provincial Commonweal Technology Applied Research Projecs of China (Gran No. 2013C33030), Humaniies and Social Sciences Foundaion of Minisry of Educaion of China (Gran No. 12YJC630091), Zhejiang Provincial Naural Science Foundaion of China (Gran No. LQ12G02007, Z14G , Y14G020006), he Naional Naural Science Foundaion of China (Gran No ), and he Fourh Excellen Yong Talens Foundaion of Zhejiang Gongshang Universiy (Gran No. QY13-23).

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