Group Tracking and Behavior Recognition in Long Video Surveillance

Sequences

Carolina G

arate, Soﬁa Zaidenberg, Julien Badie and Francois Br

emond

STARS Team, INRIA, 2004 Route des Lucioles, BP93, 06902, Sophia Antipolis Cedex, France

Keywords:

Computer Vision, Group Tracking, Behavior Recognition, Video Surveillance.

Abstract:

This paper makes use of recent advances in group tracking and behavior recognition to process large amounts

of video surveillance data from an underground railway station and perform a statistical analysis. The most

important advantages of our approach are the robustness to process long videos and the capacity to recognize

several and different events at once. This analysis automatically brings forward data about the usage of the

station and the various behaviors of groups in different hours of the day. This data would be very hard to

obtain without an automatic group tracking and behavior recognition method. We present the results and

interpretation of one month of processed data from a video surveillance camera in the Torino subway.

1 INTRODUCTION

Group tracking and event recognition in surveillance

and security is an important research area. Dangerous

and criminal behaviors are mostly observed within

groups of people. This paper presents an approach for

group tracking and behavior recognition in a subway

station applied to long video surveillance sequences

(around 2 hours per video). As stated in (Guo et al.,

2010), in recent years, research on action recognition

focused on isolated action recognition for short videos

and (Guo et al., 2010) recently proposed an approach

for arbitrary lengths of videos. Our approach recog-

nizes events in long videos and handles multiple ac-

tions at once. The long videos used in (Guo et al.,

2010) are not more than 20 minutes long and their ap-

proach can recognize multiple actions, however one

action after another.

Our technique considers a chain process consist-

ing of 5 consecutive steps as shown in Figure 1 for

group tracking and behavior recognition. The steps

are: 1) segmentation, 2) blob detection, 3) physical

objects tracking, 4) group tracking and 5) event de-

tection, similarily to (Zaidenberg et al., 2012). Ho-

wever our approach combine the technique presented

in (Zaidenberg et al., 2012) with a global tracker al-

gorithm (Badie et al., 2012) to improve the results of

the group tracking. More speciﬁcally, after apply the

group tracking algorithm presented in (Zaidenberg

et al., 2012) we apply the global tracker algorithm

and ﬁnally the event recognition technique. In this

paper, we focus on the steps 4 and 5. For the previous

stages in the processing chain, we use a MoG-based

approach for step 1 (Nghiem et al., 2009), a connected

region approach for step 2, and an appearance match-

ing approach for step 3 (Chau et al., 2013).

Figure 1: Processing chain for videos.

(Chang et al., 2011) propose a group detection-

tracking technique based on paths producing a

weighted graph where the edges represent the pro-

bability of individuals belonging to a group. On the

other hand, they use a probabilistic motion analy-

sis technique for scenario recognition which uses the

spatio-temporal pattern. However, the events recog-

nized in (Chang et al., 2011) are very general, e.g.

group formation, group dispersion, etc. Our work

models more complex scenarios: we consider, among

others, queues at turnstiles, groups blocking a pas-

sage, etc. Ryoo and Aggarwal’s (Ryoo and Aggarwal,

2010) technique detects group formation and group

activities using a stochastic grammar. Grouping is de-

termined using spatio-temporal relations.

(McKenna et al., 2000) propose a robust approach

which tracks people as they form groups using an

adaptive background subtraction technique and using

color information to handle occlusions. Nevertheless,

they do not consider the group as a new entity because

396

Gárate C., Zaidenberg S., Badie J. and Brémond F..

Group Tracking and Behavior Recognition in Long Video Surveillance Sequences.

DOI: 10.5220/0004682503960402

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 396-402

ISBN: 978-989-758-004-8

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

their technique tracks individuals before and after the

group formation. (Zaidenberg et al., 2011) proposed

a group tracking approach which ﬁrst uses a people

detector followed by a frame-to-frame tracker (F2F).

The output of the F2F tracker is a set of trajectories of

the people which is used together with additional fea-

tures to create groups and then to track them. Howe-

ver, the human detector technique requires the videos

to be corrected/calibrated before proceeding to the ex-

periment. On the other hand, our technique does not

need any pre-processing of the video.

Vishwakarma and Agrawal in (Vishwakarma and

Agrawal, 2012) classify and list the existent event

recognition techniques. The classiﬁcation begins with

2 main categories: non-hierarchical and hierarchi-

cal. The non-hierarchical category is divided into

two classes: space-time and sequential. Then the

space-time category is divided into 3 classes: vol-

ume, trajectories and features. For the sequential

classiﬁcation there are 2 subcategories: exemplar and

state-based. On the hierarchical branch there are 3

subcategories: statistical, syntactic and description-

based. Our approach uses for behavior recognition

the framework ScReK (Scenario Recognition based

on Knowledge) (Zaidenberg et al., 2012), which is un-

der the description-based/hierarchical category. We

decided to use ScReK for the simplicity of scenario

modeling in different application domains.

2 GROUP TRACKING

The social deﬁnition of a group is people that know

each other or interact with each other. McPhail and

Wohlstein (McPhail and Wohlstein, 1982) propose

two criteria deﬁning a group: proximity and/or con-

versation between two or more persons. It is difﬁcult

to automatically detect in a video if the people know

each other or their type of interaction. This leads us

to use a derived deﬁnition on observable properties

as in (Zaidenberg et al., 2012) and (Zaidenberg et al.,

2011), hence a group is: two or more people who are

spatially and temporally close to each other and have

similar direction and speed of movement, or people

having similar trajectories.

The group tracking technique presented in this

paper is based on the one proposed in (Zaiden-

berg et al., 2012). The input of our algorithm is

a set of trajectories of the physical objects. The

approach characterizes a group through three fea-

tures: the average of the intra-object distance, the

average standard deviations of speed and direction.

These 3 features are used to deﬁne a coherence cri-

terion: groupIncoherence = ω

· distanceAvg + ω

speedStdDev + ω

· directionStdDev, where ω

,ω

and ω

are normalization parameters. We actually

need to minimize groupIncoherence value because

with a lower value, the group coherence is higher. In

the experimentation the values assigned to these pa-

rameters are: ω

= 7 and ω

= ω

= 5 (after using

cross validation), beneﬁting distance over speed and

direction similarity which are quite noisy. From this

deﬁnition, when a group has a low value of the coher-

ence criterion (the groupIncoherence), it means that

the probability of this entity being a real group is high.

Our group tracking algorithm consists of 4 phases:

creation, update, split/merge and termination. For a

robust detection of coherent groups, we use people

trajectories over a time window, called delay T . This

parameter is set to 20 frames for our experimentation.

This value was chosen as a trade-off between the need

for trajectories to be long enough but without adding

a big delay to the system.

To ﬁnd similar trajectories, we use the Mean-Shift

clustering algorithm (Fukunaga and Hostetler, 1975)

because it does not require to set as input the num-

ber of clusters. However, Mean-Shift does require a

tolerance parameter determining the size of the neigh-

borhood for creating clusters.

A trajectory is deﬁned as Tra j = {(x

, y

), i =

0. . . T −1} ∪{(s

, s

), i = 1 .. . T −1} where T is the

temporal window of analysis (or delay), (x

, y

), i ∈

[0;T − 1] in each trajectory is the position of a group

in the same frame i, and (s

, s

) = speed(i − 1, i), i ∈

[1;T − 1] is the speed of the group between frames

i − 1 and i. If k positions on the trajectory are miss-

ing because of lacking detections, we interpolate the

k missing positions between known ones. Each tra-

jectory is a point in a 2(2T − 1)-dimensional space.

Mean-Shift is applied on a set of such points.

We set the tolerance to 0.1, considering group-

ing trajectories distant by less than 10% of the max-

imum. This value is quite low because clustering is

used only to group very close people, the case where

people temporarily split being handled by the update

step described below.

The creation step consists in creating a group for

each cluster of two or more physical objects (not an-

notated as NOISE) and not yet being assigned to an ex-

isting group. In case of a single physical object la-

beled as GROUP_OF_PERSONS at frame t

− T , its trajec-

tory is analyzed through the time window , t

being

the current time. If this physical object maintains the

size of a group, or is close to other physical objects,

then the creation of a group is possible and the algo-

rithm computes its groupIncoherence. If the resulting

value is low enough, the group is kept.

The update step considers the relative evolu-

GroupTrackingandBehaviorRecognitioninLongVideoSurveillanceSequences

397

tion of a physical object and a group through the

time window (from frame t

− T to t

) using the

groupIncoherence deﬁned above. It adds the physi-

cal object under consideration to the group and com-

putes the incoherence of the obtained group through

the time window. If the new physical object is really

part of the group, the groupIncoherence value stays

low and the physical object is deﬁnitely added to the

group.

The split step considers a physical object which

moved away for a large number of frames from its

group. Then, it is highly probable that this physical

object is not going to be included into the group dur-

ing the update step. Therefore, the physical object

splits from the group. If a new group is formed from

this object and other objects that have split from the

same group, then we consider that the old group has

split into the new and what remains of the old. The

merge step considers 2 groups g

and g

which can

be merged if 2 physical objects, one in each group at

frame t

−T +k (k ∈ [0; T −1]), are linked to the same

object in frame t

−T +l (l ∈ [k + 1;T − 1]). The old-

est group between g

and g

is kept and all the physi-

cal objects of the disappearing group are included into

the remaining group.

The termination step consists in deleting the old

groups. A physical object detected at a largely out-

dated frame (e.g. t

− 5T ) is erased at frame t

− T

and the empty groups are erased also. This implies

that the groups without new physical objects for 5T

frames are erased.

At the end of the group tracking phase, we obtain

an output, which is used as the input of the behav-

ior recognition stage. The output consists of a set of

tracked groups (maintaining a consistent id through

frames). Each group is associated with 7 features

(such as the intra-object distance, the speed or the ori-

entation, among others) and is composed of detected

physical objects at each frame.

2.1 Global Tracking

In some cases, the tracking algorithm may have difﬁ-

culties to keep track of a group. For example, it hap-

pens when a group leaves the scene and re-enters or

when an element of the scene occludes the group for

a signiﬁcant time. In this case, the tracking algorithm

considers that the two groups are different and labels

them with a different ID. In order to solve this pro-

blem, we use a method called re-acquisition that tries

to connect the current IDs with the previously lost IDs

in a predeﬁned time window, based on the appearance

of each group. This step can be considered as an ex-

tension of the tracking algorithm at a larger scale.

The ﬁrst step of the re-acquisition method is to

extract relevant data from each group to compute a

visual signature. To compute this visual signature, a

descriptor based on covariance matrices is used (Bak

et al., 2011). This descriptor has shown very good re-

sults in the case of multi-camera re-indentication of

people. The second step is to arrange the groups into

several clusters depending on the distance between

their visual signature. The groups belonging to the

same cluster are then considered as the same group

because their visual signature is nearly identical.

The main advantage of the re-acquisition method

is to reduce the tracking errors due to occlusions

by merging the IDs of groups representing the same

group of people.

3 BEHAVIOR RECOGNITION

The behavior recognition stage used in the present

approach is the one proposed in (Zaidenberg et al.,

2012) named ScReK. (Zaidenberg et al., 2012) iden-

tify 2 phases in a behavior recognition process: the

application knowledge (what are the expected ob-

jects? what are the event models?) and the event

recognition algorithm. They consider that knowledge

should be modeled by vision experts (specialists in

vision algorithms) together with and application do-

main experts (specialists in the expected events of

their domain).

The knowledge is modeled through the ontol-

ogy shown in Figure 4 and the grammar proposed

in (Zaidenberg et al., 2012). The ontology is imple-

mented with the ScReK declarative language. The

grammar describes the objects and events using the

extended BNF (Backus Naur Form) representation.

The objects are deﬁned using an inheritance mech-

anism: the object O

inherits all the attributes of its

parent O

. The attributes are deﬁned based on 11 ba-

sic types: boolean, integer, double, timestamp, time

interval, 2D point (integer and double), 3D point (in-

teger and double), and list of 3D points.

On the other hand, with ScReK we also represent

the knowledge of the event models. They consist of

6 parts: (1) Type of the scenario (4 values are possi-

ble: PrimitiveState, CompositeState, PrimitiveEvent,

CompositeEvent). (2) Name of the event model. (3)

List of physical objects involved in the event. (4) List

of sub-events composing the event model. (5) List

of constraints for the physical objects or the compo-

nents (6) Alarm level giving the importance of the

scenario model (3 values are possible: NOTURGENT,

URGENT, VERYURGENT). This is illustrated with the ex-

ample presented in Figure 3.

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

398

The behavior recognition algorithm deals with

spatio-temporal constraints on the detected groups.

The algorithm uses optimal event models due to the

restrictions of: maximum two components and one

temporal constraint (Allen’s algebra) between these

components. Using this optimization, the algorithm

generates an event model tree. The tree deﬁnes

which sub-event (component) triggers the recognition

of which event: the sub-event which happens last in

time triggers the recognition of the global event. For

instance, if the event A has two components B and

C with constraint: before C, then the recognition of

C triggers the recognition of A. The tree triggers the

recognition of the only events that can happen, de-

creasing the computation time.

4 EXPERIMENTS

The experiments of our research are oriented on

demonstrating the robustness of our approach and due

to the large amount of data that we have processed, it

was possible to generate statistics. In fact, we have

applied the process chain (see Figure 1) on the dataset

recorded in January 2011 for an European project in

the Torino subway, on the camera named Tornelli.

Recordings contain 1 month of data and were done

from 2010/12/30 to 2011/02/03, from 7 to 11am and

4 to 10pm. We have processed 241 video chunks of 2

hours, that is to say 482 hours of video. We focus on

analyzing the results obtained in the group tracking

and event recognition stages.

The behavior recognition process needs the model

of the group events that we want to recognize in the

scene Tornelli. Several of these events use the deﬁ-

nition of predeﬁned contextual zones in this scene, as

shown in Figure 2.

Figure 2: Contextual zones of the Tornelli view.

Based on the deﬁned zones, we have mo-

deled scenarios, representing interesting trajecto-

ries of subway users. For instance, the scenario

Group

Hesitating Hall represents groups going ﬁrst

to the turnstiles and then returning to the hall instead

of going through the turnstiles. It appears that most of

the time, when someone does not have a valid ticket

then needs to come back to use the vending machines

across the hall and after the group goes through the

turnstiles. This scenario is expressed as follows, in

Figure 3:

Figure 3: Example of the model event: Group Hesitating

Hall.

Considering the above, we obtain the ontology for

the Tornelli view. In Figure 4 we show an extract of

the ontology obtained.

Figure 4: Extract of the proposed group event ontology for

the Tornelli view.

Using our approach in the dataset mentioned

above, together with the ontology shown in Figure 2,

it was possible for us to recognize multiple behaviors

at once in each processed video.

4.1 Results and Interpretation

The number of accumulated groups over the record-

ing period per hour is shown in Figure 5: the ﬁrst re-

sult of this study is to conﬁrm that the peak hours are

indeed 7-8 am and 5-6 pm. It can also be noticed that

people tend to travel in groups rather in the afternoon

than in the morning. Indeed, people traveling in the

morning are mostly people going to work, when af-

ternoon travelers are more likely to be tourists or peo-

ple traveling for leisure. This tendency is conﬁrmed

by the generally higher number of groups observed

GroupTrackingandBehaviorRecognitioninLongVideoSurveillanceSequences

399

in the afternoon compared to the morning. Figure 5

also plots the difference in the number of groups on

weekends (Saturdays and Sundays) and on weekdays

(from Mondays to Fridays). The result, as expected,

conﬁrms that the number of groups is lower on week-

end mornings and heigher on weekend evenings than

during the week.

Figure 5: Number of detected groups per hour of the day

and difference between weekends and weekdays.

We have measured which activities are the most

frequent depending on the hour of the day. In the

next graphs (Figures 6 to 12), the x axis represents the

hour of the day and the y axis, the percentage of oc-

currences of the given activity within all activities de-

tected in the given hour (listed in Figure 4). We count

as an occurrence the detection of a group performing

an activity (at a given hour). The same group can per-

form several activities at the same time or at different

times. For instance, a group can be detected as “Walk-

ing” and “Erratic Group” because “Walking” refers to

the speed, whereas “Erratic Group” refers to the tra-

jectory. Similarily, the same group can be “Walking”

at a point in time and “Standing Still” at another.

Figure 6 compares 3 categories of speed of move-

ment and 3 agitation levels of groups. This compar-

ison shows ﬁrst that the speed of groups does not

depend much on the time of day. The most repre-

sented category is “Standing Still” where the speed

(s) is the lowest (s < threshold

). The next most

frequent category is “Walking” (which also includes

running since all speed above a threshold is included

in it: s > threshold

). Finally comes the mid-

dle category “Moving” corresponding to slow mo-

tion (threshold

6 s < threshold

). The values for

threshold

and threshold

are deﬁned considering the

interval [minimum, maximum] for people speed in this

type of videos, the interval is divided in 3 equivalents

parts. This is the moment to remind that the same

group can perform several activities. When groups

go through the turnstiles, they almost always stop for

a few seconds. An individual going through the turn-

stiles has, most of the time, to signiﬁcantly slow down

or stop for a short time. In a group, all the members

have to go through for the group to move on. Hence

the stopping time is increased in the case of a group.

The high frequency of the “Standing

Still” activity is

explained by the turnstiles visible in this view.

The agitation level is represented by the variation

of the size of the bounding box of a group. We con-

sider 3 categories from no agitation (“Calm Group”,

having a bounding box with very stable size) to

little agitation (“Active Group”) to high agitation

(“Lively Group”, the bounding box’s size varies a

lot, meaning that group members move around more).

Figure 6 shows that most of the time, this middle cat-

egory predominates. Groups are neither too calm,

nor too agitated. Moreover, it is more common for

a group to be lively rather than calm.

Figure 6: Comparison of speed of groups and variation of

group’s bounding boxes during the day.

Figure 7 presents the distribution of the activities

“Erratic Group” and “Crowd at Turnstiles” over the

hours of the day. Unlike the previous graph (Fig-

ure 6), this graphs’ y axis represents the percentage of

occurrences of each activity in the given hour in re-

lation to other hours (not other activities in the same

hour as it is the case previously). This Figure shows

that trajectories tend to be more erratic, that is to say

less straight, at peak hours. In fact, when more people

are present, it is harder to keep a straight trajectory.

The “Crowd at Turnstiles” scenario represents very

wide groups staying in the turnstile zone for longer

than 10 seconds. This occurs when the station is

crowded and the algorithm fails to detect the small

group structures and detects the whole crowd as one

group. Although this scenario approximately con-

ﬁrms the afternoon peak hours (5-6 pm), the morning

peak hours (7-8 am) are not well distinguished. Nev-

ertheless, the algorithm seems to be working as ex-

pected because in the evening there are deﬁnitely less

crowds. This leads to the conclusion that this partic-

ular scenario occurs even outside of peak hours and

is not representative of peak hours. Figure 8 presents

an example of a little crowd forming at the turnstiles

during peak hours. Indeed, as shows this example,

the width of a group is not enough to deﬁne a crowd

and to be more precise, we would need to estimate the

density of people inside this group.

Figure 9 shows the frequencies of the activity

“Group Stays Inside Zone” in various zones of the

scene (as shown Figure 2). This scenario is detected

when a group stays inside a zone for more than 10

seconds, not moving. One can notice that groups stay

the most in the turnstile zone, which is consistent with

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

400

Figure 7: Distribution of the “Erratic Group” and

“Crowd at Turnstiles” activities over the hours of the day.

Figure 8: Example of queue at vending machine.

Figure 9: Comparison of stagnancy zones.

the conclusion of Figure 6 (groups have to stop at the

turnstiles). The other signiﬁcant zone of stagnancy

is the waiting hall, where people meet, wait for each

other, wait for someone to buy tickets, or just stay

to chat. Once inside the station, people mostly go

straight to the trains.

Figure 10 compares several typical trajectories

that groups take in the station. The most frequent

activity is entitled “Group Hesitating Hall” and is de-

ﬁned as a group ﬁrst detected in the turnstile zone then

transitioned to the waiting hall zone. We call this hes-

itating because the group has not decided to enter the

station yet. In most cases, someone is needed to buy

a ticket. This activity occurs more frequently in the

afternoon than in the morning. This can be explained,

as already mentioned, by the fact that morning travel-

ers are mostly people going to work, hence they know

the way, have their ticket, and go straight to their

train without hesitating. Afternoon users are more

frequently unusual users, hence more prone to hesi-

tating. The similar activities “Group

Hesitating Left”

and “Group Hesitating Right” represent groups that

went from the turnstile zone to the left or right en-

trance zone. The ﬁrst four scenarios in Figure 10 de-

Figure 10: Comparison of frequent behaviors.

Figure 11: Illustration of a stagnant group: in the right en-

trance zone a group of subway security staff is stagnant.

Figure 12: Comparison of rare behaviors.

scribe various typical trajectories: entering from the

left or the right, going through the turnstiles and to

the left or right side of the station.

We can observe that here again, activities on the

right side of the station are better represented than on

the left side. This is most likely due to the harder

detection in the left side of the image.

Figure 11 shows an example of occurrence of the

events “Group Stays Inside Zone in entranceZoneR-

ight”, where the stagnant group is actually a subway

security team.

Figure 12 ﬁrst focuses on the other zones where

stagnancy was detected that do not appear clearly

on Figure 9. In ﬁgure 12, we can see that groups

sometimes stay on the right zone of the station, but

never on the left (in fact, there is only 1 occurrence

of “Group Stays Inside Zone in stationZoneLeft”).

Similarily, groups sometimes stay in the right en-

trance of the station, but never in the left. This phe-

nomenon can be explained by the quality of the im-

age. In fact, the camera lens is a little blurry on the

GroupTrackingandBehaviorRecognitioninLongVideoSurveillanceSequences

401

left side, which might explain low detection quality.

Nevertheless, Figure 12 shows that stagnancy in those

unusual zones mostly occurs outside of peak hours.

During peak hours, there are too many people for a

group to comfortably stagnate in one of those transit

zones.

Figure 12 also shows other trajectories taken

by groups but the occurrence rate of these tra-

jectories is lower than those in Figure 10. We

have detected a few groups passing through the

station without entering the turnstiles (17 occur-

rences of “Group Passing Through Left Right” and

1 of “Group Passing Through Right Left”), 2 occur-

rences of groups waiting in the hall and then leaving

the station. Finally, we have detected 8 occurrences of

groups waiting in the hall before entering the station

and taking the stairs. The rarity of these detections

might be explained by the difﬁculty to detect such

events, especially during peak hours.

5 CONCLUSIONS

We have presented an approach for group tracking

and behavior recognition. Our approach works in real

time and in long surveillance videos (1 month per

view). In the experiment section, we have shown that

our method is capable of recognizing multiple and

different events at the same time. This demonstrates

the robustness of our approach to processing videos

regardless of their length. Thanks to the simplicity of

using the event recognition language (ScReK), it was

possible to model complex scenarios for the Tornelli

scene as shown in Figure 8.

On the other hand, the amount of videos processed

allows us to analyze the results, obtaining the different

comparisons presented in the graphs of the previous

section.

We consider for future work the addition of prob-

abilities to the event recognition phase, which could

help to predict dangerous and criminal events. The

results obtained here show us a big challenge for pre-

diction of such events because there are not many ex-

amples with this kind of behaviors.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from the European Communitys Seventh

Framework Programme FP7/2007-2013 - Challenge

2- Cognitive Systems, Interaction, Robotics - under

grant agreement n 248907-VANAHEIM.

REFERENCES

Badie, J., Bak, S., Serban, S., and Bremond, F. (2012).

Recovering people tracking errors using enhanced

covariance-based signatures. In International Work-

shop on Performance Evaluation of Tracking and

Surveillance (PETS).

Bak, S., Corvee, E., Bremond, F., and Thonnat, M. (2011).

Multiple shot human re-identiﬁcation by mean rie-

mannian covariance grid. In International Confer-

ence on Advanced Video and Signal Based Surveil-

lance (AVSS).

Chang, M., Krahnstoever, N., and Ge, W. (2011). Prob-

abilistic group-level motion analysis and scenario

recognition. In International Conference on Computer

Vision (ICCV).

Chau, D., Thonnat, M., and Bremond, F. (2013). Automatic

parameter adaptation for multi-object tracking. In In-

ternational Conference on Computer Vision Systems

(ICVS).

Fukunaga, K. and Hostetler, L. (1975). The estimation of

the gradient of a density function, with applications in

pattern recognition. In IEEE trans. Information The-

ory.

Guo, P., Miao, Z., Shen, Y., and Cheng, H. (2010). Real

time human action recognition in a long video se-

quence. In International Conference on Advanced

Video and Signal-Based Surveillance (AVSS).

McKenna, S. J., Jabri, S., Duric, Z., Rosenfeld, A., and

Wechsler, H. (2000). Tracking groups of people. In

Computer Vision and Image Understanding.

McPhail, C. and Wohlstein, R. T. (1982). Using ﬁlm to

analyze pedestrian behavior. In Sociological Methods

& Research.

Nghiem, A., Bremond, F., and Thonnat, M. (2009). Con-

trolling background subtraction algorithms for robust

object detection. In International Conference on

Imaging for Crime Detection and Prevention (ICDP).

Ryoo, M. and Aggarwal, J. (2010). Stochastic representa-

tion and recognition of high-level group activities. In

International Journal of Computer Vision (IJCV).

Vishwakarma, S. and Agrawal, A. (2012). A survey on ac-

tivity recognition and behavior understanding in video

surveillance. In The Visual Computer.

Zaidenberg, S., Boulay, B., and Breemond, F. (2012). A

generic framework for video understanding applied to

group behavior recognition. In International Confer-

ence on Advanced Video and Signal-Based Surveil-

lance (AVSS).

Zaidenberg, S., Boulay, B., Garate, C., Chau, D., Corvee,

E., and Bremond, F. (2011). Group interaction and

group tracking for video-surveillance in underground

railway stations. In International Workshop on Be-

haviour Analysis and Video Understanding (ICVS).

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

402