Abnormal Event Detection using Scene Partitioning by Regional Activity

Pattern Analysis

Jongmin Yu, Jeonghwan Gwak, Seongjong Noh and Moongu Jeon

School of Information and Communication, Gwangju Institute of Science and Technology,

61005 Gwangju, Republic of Korea

Keywords:

Abnormal Event Detection, Scene Partitioning, Spatio-temporal Feature, Optical Flow.

Abstract:

This paper presents a method for detecting abnormal events based on scene partitioning. To develop the

practical application for abnormal event detection, the proposed method focuses on handling various activity

patterns caused by diverse moving objects and geometric conditions such as camera angles and distances

between the camera and objects. We divide a frame into several blocks and group the blocks with similar

motion patterns. Then, the proposed method constructs normal-activity models for local regions by using the

grouped blocks. These regional models allow to detect unusual activities in complex surveillance scenes by

considering speciﬁc regional local activity patterns. We construct a new dataset called GIST Youtube dataset,

using the Youtube videos to evaluate performance in practical scenes. In the experiments, we used the dataset

of the university of minnesota, and our dataset. From the experimental study, we veriﬁed that the proposed

method is efﬁcient in the complex scenes which contain the various activity patterns.

1 INTRODUCTION

In recent years, to develop practical surveillance sys-

tems, researchers have investigated several challeng-

ing issues such as the detection of abandoned lug-

gage (Tian et al., 2011) (Bayona et al., 2009) (Pan

et al., 2011), the person tracking (Krumm et al., 2000)

(Javed and Shah, 2002) (Sato and Aggarwal, 2001)

(Liem and Gavrila, 2014) and the unusual event de-

tection (Roshtkhari and Levine, 2013) (Cong et al.,

2011) (Mehran et al., 2009) (Zhou et al., 2015) (Li

et al., 2014). These studies have provided much help-

ful functions to the users of surveillance systems. Es-

pecially, detection of abnormal events is the one of

the important studies for surveillance systems to pro-

vide information of unusual or suspicious activities

to users. In general, abnormal events are detected

through the following procedures. Initially, activity

models for normal behaviors are constructed by learn-

ing activity patterns of objects observed in normal

videos which do not include abnormal events. Next,

the abnormality of events starts to be detected by mea-

suring similarity between input data and the learnt

models, and distinguish whether there is normal or

abnormal events by using a speciﬁc threshold.

To implement this strategy in actual surveillance

applications, we should consider two major issues.

The ﬁrst is to transform individual or group activities

in the scenes into trainable patterns. The surveillance

videos contains a lot of activities that are generated

by various moving objects. Raw information such as

color or gray images is not sufﬁcient to distinguish be-

tween normal and abnormal events. Therefore, well-

deﬁned features for representation of these activities

are required. To transform activities into useful pat-

terns, some researchers used texture information such

as a histogram of gradient (HOG) (Dalal and Triggs,

2005), spatio-temporal gradient (Klaser et al., 2008),

and mixture of dynamic textures (Mahadevan et al.,

2010). Also, spatio-temporal frequency (Wang et al.,

2012) has been considered an efﬁcient means of de-

tection. On the other hand, some other researchers

have focused on crowd activity modelling based on

ﬂow information such as optical ﬂow (Andrade et al.,

2006b) and force ﬂow (Mehran et al., 2009).

The second is to model normal events to mea-

sure a similarity. The model of normal events is con-

structed to obtain general patterns for normal event

from frame sequences which contains only normal

events. Dictionary learning (Cong et al., 2011) and

the bag of words (BoW) model (Mehran et al., 2009)

are widely used, and probabilistic methods such as

hidden markov model (HMM) (Andrade et al., 2006a)

are also adopted.

636

Yu, J., Gwak, J., Noh, S. and Jeon, M.

Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis.

DOI: 10.5220/0005720606340641

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP, pages 636-643

ISBN: 978-989-758-175-5

Figure 1: Right red boxes represent a distortion of ﬂow pat-

terns caused by distance between objects and camera. Left

blue boxes show ﬂow patterns of motorcycle and pedestri-

ans.

However, the constructed models of normal events

in the previous studies have used whole events that

are occurred within a frame without consideration of

characteristics of activities. Unfortunately, these ap-

proaches are not sufﬁcient to capture the abnormal

event in practical situations. Videos obtained from

surveillance systems can contain a lot of diverse mov-

ing objects, and these moving objects can generate

variety activity patterns. For instance, the property of

the activity patterns in sidewalks and roads are differ-

ent. Moreover, according to the geometric conditions,

such as angle and shooting distance of a surveillance

camera, these activity patterns can be distorted. In

other words, these activity patterns can be represented

differently according to distance and angle between

the camera and objects. Figure 1 shows the diverse ac-

tivity patterns caused by various moving objects and

the geometric condition of the surveillance camera.

To handle these problems, we propose a novel

method for detection of abnormal event using scene

partitioning. We divide an image into blocks of a

uniformed size, and extract spatio-temporal volume

from the blocks. Depending on activity patterns of

each spatio-temporal volumes, we gather the blocks

with similar patterns of activities, and deﬁne a re-

gion model using the clustered blocks within a frame.

By using this region model, the proposed method can

consider more practical situations which contain di-

verse types of activity patterns. The proposed method

uses spatio-temporal volumes consisting of dense op-

tical ﬂow (Alvarez et al., 2000). To deﬁne the regions,

entropy and magnitude of each spatio-temporal vol-

ume are used to represent activity patterns of each

block. We design a new algorithm based on an

expectation-maximization algorithm (EM algorithm)

(Moon, 1996) for grouping blocks with similar ac-

tivity patterns, and we use K-means clustering algo-

rithm to construct the bag of volume (BoV) of each

region. The main contribution of this paper is as fol-

low. in order to detect an abnormal event within com-

plex surveillance scenes, we propose anomaly detec-

tion method using local-region models which are con-

structed by scene partitioning.

The rest of the paper is organized as follows. In

Section 2, we present the previous works of research

on abnormal event detection. In Section 3, we de-

scribe the proposed method. In Section 4, the test

datasets and experimental results are shown. Finally,

the conclusion is presented in Section 4.

2 PREVIOUS WORKS

The detection method for abnormal events, as an im-

portant function in video surveillance systems, has

provoked a lot of attentions. The research on abnor-

mal event detection has made a lot of progress in re-

cent years. Abnormality of event is detected by means

of the likelihood ratio test with normal event model

of each abnormal event detection approach. Gener-

ally, events that are detected from previous works can

be classiﬁed into two types of events which is com-

posed of local abnormal events (LAE) and global ab-

normal events (GAE). LAE is the behaviors of indi-

viduals which are different from the neighbors. GAE

is deﬁned that the group behavior of the global scene

is abnormal (Cong et al., 2011).

To detect abnormality of event from videos,

many proposed methods are focused on describing a

uniqueness of activities by measures similarity using

the general model of normal events. To describe the

uniqueness of events within a frame, a lot of method

are proposed. Detection methods for abnormality of

events in trackable situations are difﬁcult to handle in

crowded scenes. Recently, the detection methods us-

ing tracking techniques focused on the direct use of

motion patterns in an image. (Wang and Miao, 2010)

is used Kanade-Lucas-Tomasi (KLT) feature tracker

(Lucas et al., 1981) to describe motion patterns of

moving objects. (Wang and Miao, 2010) used a his-

torical motion descriptor.

Other researches are focused on the untrackable

situations such as an extremely crowded scene. These

are focused on the modelling of group behaviors,

There are a number of methods that have been devel-

oped for GAE detection by modeling crowd activities.

(Mehran et al., 2009) represents behaviors of crowd

using the social force model (SFM) without any track-

ing method. SFM estimates the interaction force by

computing the difference between the desired veloc-

Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis

637

Input frames Regional motion pattern analysis

Input frames

Divided into blocks

Activity information

extraction

Grouping

(Clustering)

Mapping into the

scene

Extract feature

(Spatio-temporal volume)

Construction the

regional BOV

Block-level

Localization

𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅

Block-level detection

Figure 2: An overall procedure of the proposed method for abnormal event detection and localization in surveillance videos.

ity and the actual velocity. (Cui et al., 2011) models a

social behavior using the interaction energy potential.

Meanwhile, the bag of words (BoW) and dictio-

nary learning methods are commonly used to con-

struct the model of normal events. The BoW method

was changed to the bag of force (BoF) in (Mehran

et al., 2009) to incorporate interaction force. (An-

drade et al., 2006a) used the hidden markov model

(Eddy, 1996) for analysis of crowd behaviors.

Although these previous methods demonstrated

their performance in their own experiments, they

mostly focused on either LAE or GAE detection in

simple situations. Also, they construct models for

normal events using the whole events in the frames.

These approaches are inappropriate in modeling vari-

ous activity patterns which are generated from diverse

moving objects and geometrical distortion of a cam-

era. Thus, we argue that modelling of normal events

based on the local region model can help to enhance

the performance of abnormal event detection.

3 PROPOSED METHOD

In this section, we describe an abnormal event de-

tection and localization method using a scene par-

titioning based on local activity patterns. In this

paper, we focused on the abnormal event detection

of complex surveillance scenes through partitioning

the frame into several regions and modelling normal

event of each region.

Figure 2 illustrates the overall framework of the

proposed method. To partition the frame into the set

of regions, we divide frames into 3D blocks with a

uniform size, and then extract spatio-temporal fea-

tures from each block sequentially. Based on the

block-level activity information, the blocks which

have similar activity information are grouped, and

form a speciﬁc regions within a frame. Based on the

region model, the proposed method constructs mod-

els of normal activities for each region separately. By

using this models, the proposed method can detect

region-speciﬁc abnormal events.

To distinguish whether events are the abnormal

event or not, the proposed method calculates similar-

ity between the regional normal event model and the

spatio-temporal feature of blocks of each region. The

computed similarities are used to determine anomaly

of the events by comparing them with the given ﬁxed

threshold. Since the proposed method detect the

block-level abnormal events, the method can also lo-

calize the block-level position of the abnormal event

within the frames.

3.1 Scene Partitioning based on

Regional Activity Pattern Analysis

To partition a scene using activity information, we

divide the frames into the set of 3D blocks, and we

estimate activity patterns of each block indirectly by

using the dense optical ﬂow. In order to extract the

spatio-temporal features for obtaining training sam-

ples, given T frames which only contain the normal

events is divided into K clips. Each clip consists of

t frames. Each clip is partitioned into uniform-size

blocks of M × N × t. M × N is the size of the spa-

tial window and t is the depth of the volume in time

i.e., frames. In this paper, the temporal depth of the

block is equal to the temporal depth of the clip, where

these volumes of k-th clip C

can be described by

, ...,V

}, and n is the number of 3D blocks

that are extracted from the single clip.

However, raw data of the 3D block can involve re-

dundant information. Therefore, we compute entropy

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

638

and magnitude from each volume, for removing re-

dundant information and obtaining appropriate infor-

mation for activities within the volume. The entropy

can represent complexity of activity patterns, and the

magnitude provides strength of activities such as a

motion speed of the corresponding objects. To com-

pute the entropy, we calculate a histogram of optical

ﬂow (HOF) (Chaudhry et al., 2009) of each volume.

We deﬁne the entropy of HOF from each block in k-th

clip as follow:

= −

∑

j=1

i,k

∑

t=1

i,k

log

∑

t=1

i,k

(1)

where S

is the entropy of i-th spatio-temporal volume

in the k-th clip, B is the number of bins of the his-

togram. h

i,k

denotes the number of HOF in the j −th

bin of the volume V

. The entropy of HOF is inﬂu-

enced by the diversity of activity patterns. For exam-

ple, the entropy of scenes which is captured from a

highway intersection area is larger than the entropy of

a straight highway area. However, the entropy is in-

sufﬁcient to estimate overall properties of activity pat-

terns. In addition, we compute the magnitude of activ-

ity patterns within the volumes to represent strength

of activities, and it is described as follows:

i,k

avg

∑

t=1

i,k

(2)

where O

i,k

avg

denotes the average magnitude of opti-

cal ﬂow of the i-th volume in the k-th clip, H is the

number of optical ﬂows in a volume, and O

i,k

is op-

tical ﬂow of the t-th optical ﬂow in the i-th volume

within the k-th clip. By using the entropy S

and the

magnitude O

i,k

avg

, we can represent the activity patterns

which is inherent in each volume, and each volume

can be deﬁned as

= {S

, O

i,k

avg

} (3)

Unfortunately, the entropy and the magnitude, ex-

tracted from the single clip, are inappropriate to

model the all normal activities that are occured for a

long period. Therefore, we normalized the entropies

and the magnitudes by computing the expectation of

the entropy and the magnitude of all divided clips, and

the procedure is describe as follows:

∑

l=1

(4)

avg

∑

l=1

i,l

avg

(5)

where

and

avg

are the normalized entropy and

magnitude of activity patterns for the i-th block. L

is the number of clips. After computing the normal-

ized value for entropy and magnitude, the proposed

method partitions a scene into several regions which

consist of blocks that show similar activity patterns

using an algorithm based on the EM algorithm. The

algorithm is grouping the blocks using similarity be-

tween the entropy and magnitude of each block, the

number of centroid is not pre-determined before the

training. Fixed number of centroid may cause the

over-ﬁtted results in scene partitioning, and thus it can

degrade the performance for detecting the abnormal

events. We propose an algorithm to increase the num-

ber of the centroids int the training phase. Algorithm

1 describes the pseudo code of the proposed algorithm

for constructing the region model.

Algorithm 1 : The algorithm for constructing the region

model based on the EM algorithm.

Input: V ←− Set of activity vector of blocks, α ←−

The ﬁxed threshold, M ←− The number of activity

vector of the blocks.

Output: C

←− Centroid of the K-means al-

gorithm, N ←− The number of centroids of the

algorithm R ←− The label matrix for blocks in

frame.

is empty set at the beginning of algorithm

←− V

N ←− 1

←− 1

for all i ∈ M do

for all j ∈ N do

if argmin

(Distance(C

)) < α then

Update j-th centroid C

using V

←− j

else then

Create a new centroid and region label

N ←− N + 1

←− V

←− N

end if

end for

3.2 Regional Bag of Volumes

The process of identifying the most likely patterns for

abnormal events is one of the important issues. In

this paper, we construct regional BoVs for represent-

ing the general normal activity patterns of each re-

gion. Regional BoVs are independently modeled by

using the extracted spatio-temporal volume from the

3D block in each region. Through the regional mod-

eling, the regional BoVs can represent more detailed

information of motion activities in the regions.

Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis

639

To construct the regional BoV models, we use the

K-means clustering. The centroids of the clustering

algorithm are used as the codewords. Each centroid

is initialized randomly from observed spatio-temporal

volumes extracted from each region before training.

Then, by measuring the similarity between centroids

and observed spatio-temporal volumes, centroids are

updated. When training is ﬁnished, centroids are used

to the codewords of the regional BoV. We use an Eu-

clidean distance for measuring the similarity.

In this process, the number of codewords is im-

portant. A large number of codewords can not only

increase computation cost, but also can involve redun-

dant information for normal event. On the other hand,

too small numbers of codewords can not provide suf-

ﬁcient information to detect abnormal events. For this

reason, we determined the scale of the regional BoVs

through experimental simulations in this work.

3.3 Abnormal Event Detection and

Localization

To detect anomaly from events that are generated

from each block of each region, we measure simi-

larity between the input spatio-temporal volume and

regional BoV which is modeled from the each re-

gion that has the location information of the input

block. The proposed method used the Kullback-

Leibler (KL) divergence (Goldberger et al., 2003) to

compute the similarity. The similarity s

between the

spatio-temporal volume of i-th block in region within

t-th frame and the regional BoV of the region includ-

ing that block is describes as follow:

∑

j=1





||P(M

)



(6)

where P (V

) is the probability distribution model

which computed from the input spatio-temporal vol-

ume of i-th block in the region within t-th frame,

and P (M

) is a probability distribution model of j-

th codeword of regional BoV which is speciﬁed by

the region. Then, we determine abnormality of activ-

ity pattern of each block by comparing it with ﬁxed

threshold β. This determination process is described

as follow:

if s

> β then

is abnormal

else

is normal

end if.

By using this the block-level determination process,

the proposed method can specify a location which

contains the abnormal event. Moreover, by expand-

ing the determination process, the proposed method

can detect the frame-level abnormal event.

4 EXPERIMENTAL RESULT

The proposed method was tested on a public dataset

and our GIST Youtube dataset in order to demonstrate

effectiveness for abnormal event detection. In our ﬁrst

experiments, we used the publicly available dataset of

the University of Minnesota. To evaluate the perfor-

mance, we also compared the results with other ab-

normal event detection approaches such as pure opti-

cal ﬂow, social force model.

In the second experiments, used the GIST Youtube

dataset for testing in practical situations. We gath-

ered videos in realistic environment using a semantic

image crawler. The dataset is composed of 3 videos

which contain real abnormal events.

4.1 UMN Dataset

First, we evaluated our method using publicly avail-

able dataset which is released by University of Min-

nesota (called UMN dataset). The UMN dataset con-

sists of 3 different videos of crowd events. Each video

contains an escape situation in indoor and outdoor en-

vironment. Each video is composed of an initial part

of the normal events, and multiple consecutive frames

which contain abnormal events. In this experiment,

we used initial 200 frames for scene partitioning and

constructing regional BoVs.

In the stage of scene partitioning, we ﬁrst compute

the dense optical ﬂow. Next, we divide each frame

into 20 × 20 blocks, and extract 20 × 20 × 10 spatio-

temporal volume of optical ﬂow from each block. We

found optimal threshold using cross-validation. In

conclusion, α = 0.2 and α = 0.105 are used for video

1 and 2 respectively, and α = 0.1 is used for video

3. Figure 3 illustrates results of scene partitioning

of each video in UMN dataset. In the experiments,

β = 900 is used, and the size of the regional BoVs

is 20. Table 1 provides the quantitative comparison

results. The area under ROC (AUC) of our method

is 0.9938. The values of AUC demonstrate that the

proposed method is better than the methods using

the pure optical ﬂow and social force model (SFM)

(Mehran et al., 2009). Figure 4 shows the localization

results for abnormal event in UMN dataset. To detect

block-level abnormality, β = 0.3213 is used, and each

regional BoV contains 20 volumes.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

640

(a) (b) (c)

Figure 3: Results of the scene partitioning in UMN dataset.

(a), (b) and (c) are the scene partitioning results in 3 videos

of the UMN dataset.

(a)

(b)

(c)

Figure 4: Localization results of abnormal events in the

UMN dataset. Detection results in the frames are high-

lighted in red boxes. Right images are original frames. Left

images are localization results of right images.

(a) (b) (c)

Figure 5: Sample frames of the GIST YOUTUBE dataset.

(a) is ’building collapse’ scenario. (b) is frame which is

included to ’shootings’ scenario. (c) is frame of ’gas explo-

sion’ scenario.

4.2 GIST Youtube Dataset

To evaluate the performance of the proposed method

in practical scenes, we conducted experiments on the

dataset which consists of videos that are downloaded

from the Youtube. The dataset comprise of 3 videos

Table 1: Performance comparison for frame-level abnormal

event detection in the UMN dataset.

METHOD AUC

PURE OPTICAL FLOW 0.84

SFM 0.96

PROPOSED METHOD 0.9938

for abnormal events such as a building collapses,

shooting, gas explosion.

Figure 5 shows snapshots

of each video for the normal events. Each video in-

cludes sufﬁcient training data to build a pattern model

of normal activities. The ’building collapse’ video

contains 805 frame, the ’shootings’ video consists of

3,099 frames, and the ’gas explosion’ video has 4,640

frames. All frames in videos was resized to the width

of 320 pixels and height of 240 pixels.

The size of the spatio-temporal volume and the

size of regional BoVs are equal to the previous exper-

iments in Section 4.1. In ‘Building collapse’ video,

α = 0.1 is used to the threshold for the scene par-

titioning, α = 0.1 is used for ‘Shooting’ video, and

α = 0.095 is used for ‘Gas explosion’ video. Figure

7 show results of scene partitioning of each video in

the GIST Youtube dataset.

The detection results are shown in Fig. 6. The

normal and abnormal results are annotated as red and

greed colors in the indicated bars. Figure 8 shows the

localization results when the proposed method detects

the abnormal events within a frame.

5 CONCLUSION

Videos obtained from a surveillance system can con-

tain a lot of diverse moving objects, and these moving

objects can represent various activity patterns. Fur-

thermore, according to the geometric conditions of

cameras, the activity patterns can be distorted. Due

to these reason, detection of abnormal event is one

of the challenging issues in computer vision research

ﬁelds.

In this paper, we proposed the novel method us-

ing scene partitioning for abnormal event detection

in complex situations. To partition a scene, we di-

vide given frames into 3D blocks, and we compute

the dense optical ﬂow to represent activity patterns

of each block. In order to describe the characteris-

tics of activity patterns of each block and remove re-

dundant information, we computed the entropy and

magnitude of HoF of each 3D block. Finally, we

deﬁne regions through grouping blocks by measur-

ing a similarity based on the entropy and magnitude,

The dataset is available at http://mlv.gist.ac.kr/

mlvarch dataset/gist anomaly dataset

Abnormal Event Detection using Scene Partitioning by Regional Activity Pattern Analysis

641

#386 #805 #2438 #3099 #4460 #4640

#1800#1000#200

Figure 6: Qualitative results of the abnormal event detection for three videos of the GIST Youtube dataset.The top row

represents snapshots of video in the dataset. At the bottom, the ground truth bar and the detection result bar show the

abnormality of each frame in videos, where the green color denotes the normal frame, the red color describes the abnormal

frame, and the yellow color corresponds to frames which are use to scene partitioning and training the regional BoVs.

(a) (b) (c)

Figure 7: Results of the scene partitioning based on the re-

gional activity pattern analysis in GIST Youtube dataset. (a)

shows the result of scene partitioning of ’building collapse’

video. (b) denotes the result of scene partitioning for ’shoot-

ings’ video. (c) represent the scene partitioning result of

’gas explosion’ video.

(a)

(b)

(c)

Figure 8: Localization results in the GIST Youtube dataset.

Abnormal event location are highlighted in red boxes. Right

images are original frames. Left images are localization re-

sults of right images.

and model the regional BoVs independently. In this

manner, The proposed method can model the normal

events of complex situations. The experimental re-

sults showed that the proposed abnormal event detec-

tion method can detects abnormal activities success-

fully in two datasets, and demonstrate that the method

can provide an efﬁcient way to detect the abnormal

event in practical scenes.

The current version of proposed method has two

major drawbacks. First, in order to perform scene

partitioning precisely, the proposed method requires

a sufﬁcient number of observations for moving ob-

jects. When the moving objects are not observed,

or the number of the observed objects is not sufﬁ-

cient, results of the scene partitioning and the normal

event modelling can degrade performance of abnor-

mal event detection. Second, the proposed method

determines thresholds manually by users. The inac-

curate setting of thresholds can be the main cause in-

correct detection for abnormal events. However, these

drawbacks are general problems appeared in most of

abnormal event detection methodologies.

To this end, our future work is to improve the re-

sult of the scene partitioning by incorporating appear-

ance information, and develop an automated method

for determining suitable threshold values.

ACKNOWLEDGEMENTS

This work was supported by Institute for Informa-

tion & communications Technology Promotion(IITP)

grant funded by the Korea government(MSIP)

(No.B0101-15-0525,Development of global multi-

target tracking and event prediction techniques based

on real-time large-scale video analysis) and Center for

Integrated Smart Sensors as Global Frontier (CISS-

2013M3A6A6073718).

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

642

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