Abnormal Crowd Behavior Detection Using Behavior Entropy Model

Proceedings of the 2012 International Conference on Wavelet Analysis and Pattern Recognition, Xian, 15-17 July, 2012

ABNORMAL CROWD BERAVIOR DETECTION USING BERAVIOR ENTROPYMODEL

WEI-YA REN, GUO-HUI LI, JUN CHEN, HAO-ZHE LIANG

College of Information System and Management, National University ofDefense Technology, Changsha 410072, ChinaE-MAIL: [email protected]@[email protected]@hotmail.com

Abstract:Using Behavior Entropy model, we introduce a novel

method to detect and localize abnormal behaviors in crowdscenes. Our key insight is to estimate the behavior entropy ofeach pixel and whole scene by considering defined pixels'behavior certainty. For this purpose, we introduce informationtheory and energetics concept to define pixel's behaviorcertainty based on video's spatial-temporal information. Sceneentropy behavior and behavior entropy image can be used todetect and localize anomalies respectively. We discussparameters' setting by analyzing how they influence model'sdetecting and localizing abilities, and our model is robust toparameter setting. The experiments are conducted on severalpublicly available datasets, and show that the proposed methodcaptures the dynamics of the crowd behavior successfully. Theresults of our method, indicates that the method outperformsthe state-of-the-art methods in detecting and localizing severalkinds of abnormal behaviors in the crowd.

Keywords:Detection and localization; Optical flow; Behavior Entropy

1. INTRODUCTION

Abnormal behavior detection can be viewed as a type ofhigh-level operation of image understanding [10]. Intelligentvisual surveillance has got more research attention andfunding due to increased global security concerns, and it hasmade great progresses in recent years, such as backgroundmodel [25], object tracking [26], pedestrian detection [27],action recognition [28] and crowd counting [7]. WhileAnomaly detection plays an important role in airports,railway stations, shopping malls, military installations, etc.,crowd scene analysis faces even more challenges likeemergent behaviors and self-organizing activities [1][6].Monitoring videos of crowded scene is also a very expensiveand tiring task [17][18].

In the field of transportation and public safety, somewell-established models have been developed for describingthe group behaviors in crowded scenes, such as Social Force

978·1-4673·1535·7/121$31.00 ©2012 IEEE

Model [2][23], Hybrid methods and some group behaviorsmodels[3][4][5]. Mahadevan et al. [19] models normal crowdbehaviors by mixtures of dynamic textures. Recently, thecomputer vision community has focused on crowd behavioranalysis. Social force model was studied in [20] to analyzethe human interactions in order to detect abnormal events. Incrowd analysis, as one of the measurement of movement,optical flow compute the velocity of the motion objectbetween two consecutive frames of an image sequence [21],which can reflect the change of velocity. And it has got a lotof achievements for anomaly detection in crowd scenes byusing this method [13]. According to this, optical flow is usedin our model.

Entropy [12] arouses people's attention because it hassignificant meaning to detect anomaly events. Yang Cong etal. [11] compute entropy by formulating the sparsecoefficient as a probability distribution. Guogang Xiong et al.[9] build histograms on X- and Y-axes respectively, theyobtain probability distribution of the foreground object andthen define crowd entropy. Their foreground entropy denotesthe dispersion of foreground on the horizontal and verticaldirections.

In this paper, we propose a new method to detect bothlocal and global abnormal events in crowded scenes. Further,our behavior entropy not only denotes the dispersion ondifferent directions, but also denotes the individualmovement information and interact information amongindividuals. As seen in Fig. 1, several types of abnormalbehavior in crowd scene are analyzed, and we can detectthem by computing their scene behavior entropy (SBE) andlocalize them by computing their pixels' behavior entropy(BE) distribution.

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Figure 1. AnomaBy detection examples. La shows individual conversewalking in crowd, it's pixels' behavior entropy(BE) distribution isdisplayed in the right side. Lb shows crowd dispersion, it's pixels'behavior entropy(BE) distribution is displayed in the right side, it isclearly that it's scene behavior entropy(SBE) will suddenly become largewhen event occnrs.

The organization of this paper is as follows. In the nextsection we overview our model. In Section 3 we introduceBehavior Entropy model for modeling the crowd movement.In Section 4 we introduce our method to estimate the pixel'sbehavior certainty (BC) in the crowd. Section 5 describes theproposed method to detect and localize abnormal behaviorsin the crowd. In Section 6, we discuss model parametersetting and demonstrate abilities of the approach to detect andlocalize abnormal behaviors on several available datasets.

2. PROPOSED MODEL

The definition of entropy from information theory implyus the power of information. Behavior Entropy Modeldescribes behaviors in crowd scene by regarding the scene asthe result of information driving. It can be assumed thatindividual's behavior depends on individual informationobtainment and information interaction among individuals.

Information can be described by certainty, that meansthe existence of entropy, and abnormal crowd behavior isessentially an eccentric state of scene's entropy. Therefore,we defme behavior certainty (BC) and behavior entropy (BE)to capture and excavate useful information in scene. Noticethat behavior entropy is different from common entropybecause ofits novel thoughts and computing method.

It can be assumed that pixels whose optical flow equalto zero do not contain useful information. If a pixel's optical

flow is not zero, we named it behavior particle, or particle asshort, and scene's information are all reflected by particles.We assume that scene's information is all known as a prioriknowledge, however, it is impossible because of theexistence of particles. Particles bring movement informationand somehow mean the loss of scene's information whichreveals behavior in crowd. Therefore, we can representscene's behaviors through finding the loss of information out.

In this way, the initial value ofPixels' behavior certaintyis set to 1 and there is only need to update particles' behaviorcertainty to scale the scene's loss information. Then, everyframe in video will have a pixels' behavior entropydistribution, and scene behavior entropy (SBE) can becalculated by pixels' behavior entropy.

In a crowd scene, we detect abnormal behavior byanalyzing the change of SBE (scene behavior entropy) intime and localize abnormal behavior by pixels' behaviorentropy distribution.

As seen in Fig. 2. By using Hom-Schunck method [16],we firstly compute optical flow because it reflects video'sspatial-temporal information, and our model regards it as asort of foundation. The most important and key thing is tocompute pixels' behavior certainty, then we computebehavior entropy which includes pixel's BE (behavior) andscene's SBE (scene behavior entropy). The last but not theleast, anomalies will be detected and localized throughbehavior entropy.

Anomally Detectionand Localization

Figure 2. Proposed model for abnormal behavior detection in thecrowd videos.

This paper aims to present an effective model to detectboth local and global anomalies in public scenes. Eventhough our work uses entropy concept, we use a completelydifferent course by estimating defined behavior certainty inthe crowd and detect anomalies directly without segmentation.There is no need to acquire a mass of data to depict normalscene's character and our model has a unified framework forvarious anomaly detection and localization.

3. BEHAVIOR ENTROPY MODEL

3.1 Optical Flow

Optical flow is an effective method in detecting andcalculating the motion of each pixel between two frames, andthus provides a possible way to get the velocities of themotion objects [22]. By using Hom-Schunck method [16],two images will be analyzed and the values of the variable u

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and v are produced, where u is the flow in x-axis and v is theflow in y-axis. In this paper, consider pixel i's u and v are- - -denoted by Vix and Viy respectively, written Vi =(ViX, Vs).

3.2 Behavior Particle

We put particles on pixels whose optical flow not equalzero, then we named these pixels as behavior particles,"particles" for short. As discussed before, the model's maintask changes to compute particles' behavior certainty insteadof computing all pixels' behavior certainty. It is easy to knowthat pixels conclude particles (behavior particles), andbehavior particles are helpful because it means that our modelwon't take up too much computing time.

4.1 Interaction Information

Interaction effect among particles can reflect informationof the crowds, it inspires us to consider particles' directionfactor to defme particles' BC at first. We assumed that BC(behavior certainty) of particle i is related to its own directionand directions of particles which around it. Particles thataround particle i are limited into an area which we calledDi .

Di contains k, particles, and its shape can be rotundity,

rectangle or other symmetrical form. The appropriate size ofDi will be discussed in section 6.

In order to simply consider the direction factor, we can

standardize~x and Viy as follows:

3.3 Defmitions in the Model (3)

(4)

- -It is easy to know IVi 1=1 ifwe note Vi =(Vix'Viy ) .

Particle's behavior entropy (BE) can be computed

thr ghP as formula (1) explains. For lim(-P.log

2P)=O,ou I P;.....O 1 1

we can draw fig. 3.a. which is the plot of ~ versus BEj

• The

curve helps us fmd the way to compute~. BEj

gets largest-1

value when ~ equals to e . For the reason that we assumed

the behavior certainty should have a lower boundary, we can

make ~ always be more than or equal to e-1• It is also

convenient that the curve become monotone decrease.Particle will make largest contribution to scene behavior

entropy (SBE) when it equals to e-1•

BC ofparticle i is firstly given by the following equation:

li=e-1+1]-(I-e-1) , O~1]~1 (5)

Where 1] is defined as:

k· k·TJ= 1!:~i 1I!:I ~i I, I~i 1* 0 (6)

i=l i=l

Apparently 0 ~ 1] ~ 1 . Furthermore, equation (6) can

be written as:

(2)

(1)

M

SBE=-L~IO& ~i=1

4. ESTIMATION OF PIXEL'S BEHAVIOR CERTAINTY IN THE

CROWD

As we claimed before, we assumed that we know allscene's information as a priori knowledge, however, onlyparticles whose optical flow do not equal 0 contain usefulinformation which can reflect the loss of information.Through fmding the loss information, scene's events can berepresented. Since we initialize Pixel's BC (BehaviorCertainty) equal to 1, it is important to fmd a way to updatethe BC values. Two main clues are considered, one is theinteraction information, and the other one is state'sevolvement if every particle is regarded as a subminiaturesystem.

Definition 1 Behavior Certainty (BC) of Pixel i is theprobability of keeping its former state.

The key problem is to understand and compute BehaviorCertainty (BC), we denote it as~ . In next section, probability

~will be illuminated in detail.

Definition 2 Behavior Entropy (BE) of Pixel i is defined asfollows:

Definition 3 Scene Behavior Entropy (SBE) is accordingto the following equation:

(7)

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1] describes direction factor among particles in area Di .

In area Di ' particles' behavior certainty (BC) ~ equal to 1

if all particles have the same direction. When particles in Dihave equivalent value in any two opposite directions,

Particles' BC P becomes to e-1 and particles' BE arrive toI

its largest value. However, particle's BC neither equal to 1

nor e-1 in most situations, in fact it always fall in between

e-1 and 1.Fig. 3.b. show the relationship between particles'

behavior certainty ~ and 1] . ~ shows a linear correlation of

1] , and we can infer that e-1~ r: ~ 1.

b. The plot of Pi versus TJ

(10)e-E/~

~n e-EilTkL-Ji=l

Pj(Tk ) _----

~(Tk)

e-E,ITi (9)P;(fkF :E:,e-EiIT,

Consider two states i and j of system S, the ratio of theirprobabilities is:

In order to depict the loss of scene's information, wedefmed particles' BC (behavior certainty) in equation (5),however, it only considers the interaction information amongparticles, so it is necessary to consider other information.Considering particles' former BC value and velocity factor,we revise particles' BC based on concept of system state. Weassume that system will probably change its state from astable one to an unstable one as annealing process.

Each particle is regarded as a subminiature system whichalways has two states, as seen in fig.4, state 1 and state 2,they each correspond to a BC (behavior certainty) value:

~which is computed by equation (5) and constant value e -1 •

States' energy equals BE which is calculated respectivelywith their corresponding BC value, so we know that their

energy are.E, =-~lo~, E2=-e-

110ge-1=e-11ln2.

"1o

e~' ------------------1

-1e

a. The plot of BEi versus Pi

Figure 3. Function curve illustration. 3.a shows the relationship

between BEi and ~ ' BEi=-~·I~~ ' and lim(-~.log2~)=O .~---)()

BE -1 -1i get its largest value e /ln2 when ~ equal to e . 3.b displays the

linear correlative relationship between ~ and 1] , it shows ~ 's value range:

e-1 < P. <1.,

4.2 State Transition

BCi=~

E1 =-P)o~State 1S;

E2=e-1/ln2 State

E1 = -P)ogPr············State 1S;

E =e-1/ln2- - State2 BC

i=e-1

Particle's behavior certainty can be apprehended astemporary state of particle, and all possible states of particlewill form a system. With help of the concept of system,analysis of particle's behavior will be easier. In order todescribe a system in an effective way, concept of state andannealing process [14] in energetics is borrowed to excavatethe hidden useful information of scene.

Annealing process: Thermodynamic system S has nstates which are limited and discrete, the energy of state i isE

i, system will go into thermal equilibrium after a while

with temperature ~' then the probability system being state i

is:Pi(Tk)~ke-~/~ (8)

Consult Boltzmann equation [15], we know that:

Figure 4. State transition illustration. Subminiature system Si stays in

state 1 at first and then jump to state 2 with probability Po . After the

possible transition process, Si evolves into S~ . Solid line in S~ means new

system's state, and dashed line means the state which has been abandoned by

system Si'

Apparently, state 1's energy is always lower or equal tostate 2's. It means state 1 is more stable. We assumed thatsubminiature system will stay in state 1 at first and then jumpto state 2 with probability Po. This process can be interpretedas a possible transition, though particle tends to keep itsprevious state in this process for it is more stable, transitionmay occurs. Particle's fmal BC will be fmal confrrmed afterthis process, for we set particle's BC be the value that

215


evolved system S~ corresponds to. In other words, particle's

BC will probably change to e-1 as its final value withprobability Po as system evolves.

BC ofparticle i is fmally given as follows:

abnormalities in the abnormal frame by locating the pixels ofhigh BE value.

5.3 Algorithm Flow

Model's main algorithm is shown as follows:

According to equation (10), we let Tk; =;Lezr

, where

A"r are constants, and % is the average absolute velocity of

particles in areaz, ,written Z = ~ ,i E Z; ,by the way, Z, is

different from Di

• So we can confirm Po as follows:

5. DETECTION AND LOCALIZATION OF ABNORMALITIES

In fact, Po reflects the degree of the loss of information,

and materializes to the computing of particle's BC. System ismore stable when its energy is lower, from formulae (11) and(12), we can see state is harder to change when particle's BCget larger or IVi Iget smaller.

{e-1

, with probablity Po

J: = J:, with probablity I-po

Po =e-{E;-~)/J-z'

-{e-1_lnr 1- B _1og~.-I)/J-z'=e ~ ~

(11)

(12)

Main Algorithm

Input: Adjacent frames of video

Output: behavior certainty(BC), behavior entropy(BE), scene behaviorentropy(SBE)

1: Initialize ~=1

2: for i=O,1,2,... ,m

k,.... k , ....4: ~ = e -I + 11-(1- e-I) , 11= I~ V; I/~ I V; I

i=1 ;=1

7: end if

M M

8: SBE=LBEi=-Lli log21ii=l i=1

9: end for

5.1 Anomaly detection

According to defmitions in our model, SBE (scenebehavior entropy) can be calculated and we can get a curve ofSBE versus frame. However, discrete value of SBE is not aclear evidence of abnormal behaviors, in order to improvedetecting effect, we smooth SBE curve to deal with detectingtask Aiming at specifically scene, threshold value is settingto make the final decision. It is also easy to get ROC curvethrough changing threshold value and AUC can be calculatedwhich can used to judge model's performance.

5.2 Anomaly Localization

By judging SBE curve, we distinguish abnormal framesfrom the normal frames. After that, BE (behavior entropy)distribution is really helpful to localize regions in the framethat correspond to the abnormalities. As we discussed earlier,the BE distribution reveals the useful information in the scene,which corresponds to the activities in the scene. In anabnormal scene, we expect the anomalies to occur in regionswith higher BE (behavior entropy). Therefore, we localize

6. EXPERIMENTS AND DISCUSSION

It is significant to detect global and local anomalies witha union model. Anomalies can be identified as irregularevents from normal ones, thus, behavior entropy model is toidentify abnormal events no matter what kind of it. There areseveral parameters in this paper, and they are importantbecause they may influence the model's performance. We setDi,Zi's shape squares with center i, square will change to

rectangle when i approaches to image's boundary, otherparameters' setting will be discussed later in detail. Inexperiments, we set suitable parameters based on scene'sspecialty and detecting demand.

We use UMN and Crowd_PETS09 datasets for globalabnormal events' detection and localization, and then useUCSD dataset and an internet dataset for local abnormalevents' detection and localization. All videos have toeliminate noise first, and all experiments in this paper run ona computer with 4GB RAM and a 2.3--2.9GHz cpu. The

216


average computation time is 1.5s/frame for datasetsexcluding optical flow computing time.

6.1 Global Anomalies

6.1.1 Tests on UMNDataset

The approach is tested on two datasets, one of them is apublicly available dataset of normal and abnormal crowdvideos from University of Minnesota [24]. The datasetcomprises the videos of 11 different scenarios of an escapeevent in 3 different indoor and outdoor scenes, there are 7739frames at all. Each video consists of an initial part of normalbehavior and ends with sequences of the abnormal behavior.

As seen in fig.5, 5.b is the smoothed video's curve ofSBE (scene behavior entropy). In Fig.5.a and 5.c, we showseveral abnormal and normal original frames respectively anddisplay their BE (behavior entropy) distribution which is usedto localize the abnormal regions. In BE (behavior entropy)distribution, red pixels correspond to the highly probableabnormal regions.

With help of SBE curve, we detect abnormal eventsearlier than some classic methods [2][29]. For instance, wefirst number all 11 scenarios: 1.1--3.3, as seen in fig 5.b, inscenario 1.1, the abnormal running is detected at the 483thframe, in scenario 2.6, we detect the abnormal running at4781th frame, and in scenario 3.2, we detect abnormalbehavior at 6826th frame.

Table 1 provides the quantitative results of thecomparison with the state-of the-art methods. We use Areaunder ROC Method to display global abnormal behaviorsdetecting performance of our model. It is obvious that ourmodel can get a high score in detecting global anomalies.

TABLE 1. THE COMPARISON OF THE USE OF THE PROPOSED BEHAVIORENTROPY METHOD AND THE STATE-OF-THE-ART METHODS IN THE UMNDATASET

and high-level analysis, where high-level analysis meansdetection of separate flows and specific crowd events. Thenwe select a dataset which contains gathering and dispersionevents in dataset S3 \High Level\Time_14-33.

Method Area under ROC

Behavior Entropy O. 99

Chaotic Invariants[29] 0.99

Social Force [2] 0.96

Pure Optical Flow[2] 0.84

6.1.2 Tests on Crowd PETS09 dataset

In order to observe behavior entropy model'sperformance on global abnormal events further, we choose afamous datasets named PETS 2009 [30]. PETS 2009's themeis multi-sensor tracking and event recognition in crowdedpublic areas. It includes low-level analysis, mid-level analysis

Figure s. Some results of experiments. S.c shows several normal framesand their corresponding BE distribution image. In S.b, video's SBEcurve is illustrated and we use independent threshold value to makejudgement because of different shoot time means different scene, wefirst smooth SBE, and the span for the moving average is setting as 10.S.a shows several abnormal frames and their corresponding BEdistribution image which can be used to localize abnormal behaviors.Red values represent higher BE(behavior entropy) whereas blue valuesrepresent low BE. The results show that the BE distribution are capableof locating the abnormalities in the regions that are occupied by thecrowd.

Fig. 6 shows the main experience result, 6.b is thesmoothed video's curve of SBE (scene behavior entropy). InFig. 6.a and 6.c, we show several abnormal and normal

217


original frames respectively and display their BE (behaviorentropy) distribution images. When gathering and dispersionhappens, the scene' SBE value both rise relative to theirnormal scene.

that the proposed method outperforms the state-of-the-artmethods to distinguish abnormal sequences exactly.

By means of SBE(scene behavior entropy), detectingabnormal events in datasets become simple and feasible, asseen in fig. 7, we show a sample of UCSD ped_1 video 27whose abnormal event is a running car in crowd.

Figure 7. A detecting sample. SHE curve of UCSD ped_l video_27.With the setting of the suitable threshold, it is eay to detect the car fastly.In this sample, we have not smooth the SHE curve. However, in mostsituation, smoothing is necessary.

TABLE 2 THE COMPARISON OF THE USE OF THE PROPOSED BEHAVIORENTROPY METHOD AND THE STATE-OF-THE-ART METHODS IN THE UCSDDATASET.

We compare our method with MDT, Social force etc. astable 2 shows. It is easy to find that our AUC outperformsthe state-of-the-art methods. Some image results in UCSDped_1 are shown in fig. 8, and we compare MDT approach[19]. And in UCSD ped_2, as seen in fig. 9, bikers, skaters,small cars, etc. are detecting with high accuracy.

Figure 6. Some results of experiments. In 6.a, video's SHE curve isillustrated and we use independent threshold value to make judgementfor gathing and dispersion respective , we first smooth SHE, and thespan for the moving average is setting as 10. In 6.b, gathering anddispersion scene each have a normal frame and a abnormal frame, theirHE distribution images are shown below them.

The ability of detecting global abnormal events has beenshown by over two datasets. Behavior entropy model can fastand accurate detect anomalies, and the BE distribution imageare capable of locating the abnormalities in the regions thatare occupied by the crowd.

6.2 Local Anomalies

6.2.1 Tests on UCSD Dataset

Method

Behavior Entropy

MDT [19]

Social Force [2]

Adam et al. [31]

Area under ROC

0.7950

O. 7895

O. 7414

0.6350

UCSD database [19] is used to enable the evaluation ofperformance with respect to the ability to localize anomalies.In the normal setting, the video contains only pedestrians.Commonly occurring anomalies include bikers, skaters, andsmall carts. The data was split into 2 subsets: UCSD ped_land UCSD ped_2, each corresponding to a different scene.

To test localization accuracy, detections are compared toframe level ground truth masks. Area under ROC curves(AUC) can evaluate the anomaly detection performance, andit is based on these detection and false positive rates, formultiple threshold values. The AUC in table 2 demonstrates Figure 8. Examples in UCSD ped_1. Comparisons of abnormal

localization results from (i) our approach; (ii) MDT approach[19].

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TABLE 3 FOUR ABILITIES WHICH CAN REFLECT ONE MODEL'S PERFORMANCE

ON DETECTING LOCALIZATION VARIOUS ABNORMAL EVENTS.

Abili Illustration

Figure 9. Examples in UCSD ped_2. Abnormal objects are detectedwith high accurancy and low error with our approach.

6.2.2 Tests on Recessive walking sequence

We download a sequence about recessive walking and useit as a type of local abnormal event to evaluate our model'sperformance. Fig. 10 and fig.I I shows some results of thisexperiment. The detecting time and localization effect areall that could be desired.

A Ability of detecting abnormal interaction information

B Ability of detecting abnormal movement information

C Ability ofreducing false alarm

D Ability of reducing false dismissal

Assemble of parameters may be different and it isinfluenced by crowd density, shooting angle, camera positionand frame variable rate. There are 4 main parameters in ourmodel, and assemble of them will embody an integrationcapability. As seen in fig.12, through vast of experiments andcontrol variate method, we get several qualitative curves withparameters and abilities. So it is really an interesting task toget a perfect parameters assemble.

o

DD

A

B

B

A C C

A- 0 o,

Figure 10. Video's SHE curve is illustrated and we use asituable threshold value to make judgement for recessive walking , wefirst smooth SHE, and the span for the moving average is setting as 10.

Abilities

o

D

CA

B

r

Abilities

o

A---.::--

C

D

Figure 11. Examples in recessive walking sequence,Pedestrian who walks in an opposite direction is detected fast andlocalized well.

6.3 Parameter analysis and setting

Anomalies include global and local abnormal event, andanomalies' types are abundant. So it is more important toanalyze what the anomalies detecting requires, we classifythe requirement into four abilities shown in table 3. Whenone model gives consideration to these four abilities well, itwill really stand out.

Figure 12. Qualitative curves with parameters and abilities.There are four parameters and four abilities at all.

Parameters setting in experiments are revealed in table4.Special instruction: UNM dataset is of exactly the sameparameter setting as recessive walking dataset. Itdemonstrates the model's robustness of parameter setting.

TABLE 4 PARAMETER SETIING IN DIFFERENT EXPERIMENT. THERE ARE

FOUR DATASETS AND FOUR PARAMETERS AT ALL. THE SETTING OF Z i IS

UNIFORM IN FOUR DATASETS.

A- r D; 's side Zi 's side

length(pixels) length(pixels)UMN 0.3 1 30 10PETS 2009 0.001 5 200 10Recessive walking 0.3 1 30 10UCSD 0.15 5 1 10

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Fig. 13 reveals that the parameter combination willinfluence the model's results. Fig. 13.a is an original frame inUCSD dataset, and then there parameter combinations areshown. The first parameter combination detects both skaterand recessive walking individuals. The second combinationjust detect skater and the third one just detect recessivewalking.

Figure 13. A samlpe of parameter combination. 13.a displaysan original frame in UCSD dataset Ped_2. b shows a BE(behaviorentropy) distribution image with one kind of parameter setting, modeldetect both skater and recessive walking individuals. 13.c and 13.dindicate another two different parameter combination, they overlapbetween BE distribution image and original frame, and each of themonly detect one kind of the anomalies.

7. CONCLUSION

Experiments reveal frame's SBE (scene behaviorentropy) will rise when running, dispersion, gathering andregressive walking occurs. Rising of SBE reflects loss ofinformation, it means abnormal events always wreck normalscene's information though it brings abnormal information,so our mission is to know how to calculate the loss ofinformation. Behavior Entropy Model is effective andpredominant for its well performance on detecting andlocating abnormal events, and the most important thing is stillto construct a uniform framework aiming at variousanomalies.

Manifestation mode of information includes video, voice,etc. In future, we can compute their behavior entropyrespectively and use their joint distribution to complete thedetecting task.

Acknowledgements

This paper is supported by College of Information Systemand Management, National University of DefenseTechnology and subsidized by National Natural ScienceFoundation ofChina (Grant No. 61170158).

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