1. technology and computing
2. computer security
3. antivirus and malware

ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
An Novel Algorithm for Detecting the Suspicious
Acts in Crowded Scenario
V.G.Janani,
Assistant Professor
Electronics and Communication Engineering
Velammal College of Engineering & Technology
Madurai,Tamilnadu,India
P.Malarvizhi,
Final Year Student
Electronics and Communication Engineering
Velammal College of Engineering & Technology
Madurai,Tamilnadu,India
K.Saranya,
Final Year Student
Electronics and Communication Engineering
Velammal College of Engineering & Technology
Madurai,Tamilnadu,India
K.Lavanya,
Final Year Student
Electronics and Communication Engineering
Velammal College of Engineering & Technology
Madurai,Tamilnadu,India
Abstract—Crowd analysis becomes the most active-oriented
research and trendy topic in computer vision nowadays. Long
term human monitoring in crowded scenario is impractical and
ineffective. Automatic abnormal motion detection using this
novel algorithm is therefore the key for successful in video
surveillance in dynamic scenario like airport terminals. The aim
of this paper provides a novel solution to abnormal detection in
real time video surveillance. We proposed an algorithm called
Histogram Oriented Particle Flow for motion detection. A fast
version of this algorithm is based on fusing the particle flow with
background subtraction step. Motion features are derived from
particle flow method. Finally a one class non linear SVM is
applied for the classification of suspicious behaviour in crowded
scenario.
Keywords-Abnormal Detection, Histogram Oriented Particle Flow
(HOPF), Non Linear One Class SVM
I.
INTRODUCTION
In many applications, such as video surveillance, content
based video coding, and human–computer interaction, moving
object detection is an important and fundamental problem. The
general technique for moving object detection is background
elimination under the situation of fixed cameras. Detection of
moving objects in video stream is the first related step of
information removal in many computer visualization
applications, including video surveillance, people tracking,
traffic monitoring, and semantic annotation of videos. Video
cameras are extensively used in surveillance application to
examine public areas, such as train stations, airports and
shopping centers. When crowds are intense, automatically
tracking individuals becomes a difficult task. Anomaly
detection is also known as outlier detection, which is
applicable in a variety of application. Activity analysis in
video sequences means interpreting human or moving object
behaviors and, specifically, detecting abnormal events, which
is the focus of this paper. Abnormal events are defined to have
the following properties: They are rare and they are
unexpected. According to this definition, examples of
abnormal events include a person’s slip or fall, a vehicle
driving on the wrong side of the road, and people running
abruptly. Among many possible abnormal events, we focus on
events that have different speeds and directions compared to
normal situations. There are generally two approaches to
detecting abnormal events: an object-based approach and a
feature-based approach. The object-based approach attempts
to detect and track moving objects individually from video
sequences. In contrast to the object-based approach, the
feature-based approach extracts low-level motion features
instead of tracking each moving object individually.
77
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
For abnormal event detection tasks in video, we propose a
Descriptor encoding the movement information of the global
frame. Moreover, we adopt Histogram Oriented Particle Flow
method, one-class support vector machine (SVM), to
distinguish the abnormal event from the reference model. The
rest of the paper is organized as follows. In Section 2, related
works are briefly reviewed. In Section 3, the proposed
descriptor, Particle Flow is described to provide feature
vectors for classification algorithm. In Section 4, we present
experimental results on real world video scenes. Finally,
Section 5 concludes the paper.
II. RELATED WORKS
The detailed literature survey of our work is presented in
this section. Abnormality detection is classified into two
categories; trajectory analysis and motion analysis. Trajectory
analysis is based on object tracking and typically requires
normal environment to operate. Motion analysis is better
suitable for crowded scenes by analyzing patterns of
movement rather than attempting to distinguish object. Some
of the few existing works consider the relationship between
pedestrians’ social behaviors and their walking scenarios.
Recently, some methods [1], [2] utilize crowd flow and
semantic scene knowledge to detect abnormal activity and
obtained good results. But these methods can be only applied
for some simple scene (e.g. single sink/source, single crowd
flow). There have been attempts to model crowds based on
discriminative classifiers [3].The analysis of crowd behavior
and movements are of particular attention in video
surveillance domain [4].There are two main approaches in
solving the problem of understanding crowd behaviors. In the
conventional approach, which we refer as the “object based
“methods, a crowd is considered as a collection of individuals
[5],[6]. Therefore, to understand the crowd behavior it is
necessary to perform segmentation or detect objects to analyze
group behaviors [7].
Crowd-related scene understanding problems, such as
crowd segmentation [8], [9], crowd counting [10], movement
tracking in crowd [11], [12], and crowd activity perception
[13], have attracted the interest of many researchers. More
relevant to this paper are works on crowd motion pattern
extraction and abnormal event detection. There are a few
methods which directly extract motion patterns from optical
flow fields. In [14], video frames are initially characterized by
the histograms of the corresponding optical flow fields, and
further represented as points on a low-dimensional manifold
through a spatial-temporal Laplacian Eigen map method.
A video is thus represented as a trajectory of frames on the
manifold. After learning the trajectories of videos depicting
normal events, abnormal events can be detected by comparing
the trajectories of the videos with those of normal videos.
Cong et al. [15] introduced a multi scale histogram of optical
flow based on three different spatial-temporal templates to
extract optical flow field patterns of normal crowd behaviors,
and adopted a sparse method to determine a pattern subset as a
dictionary which can be used to reconstruct other elements.
Since only normal data are used for dictionary construction,
anomaly can be detected by comparing the sparse
reconstruction cost of a set of given data with a pre specified
threshold. Yassine et al. [16] proposed a direction model to
extract dominant directions in a block of optical flow field.
The novelty of this model is the use of multiple directions
descriptor at each position, which is suitable when there are
multiple moving objects with different movement directions in
a local region. After combining the blocks, a crowd of people
is segmented into several groups, each of which corresponds
to a particular type of movement pattern. The overall crowd
behavior is finally recognized through analyzing the
movement directions and speeds of the groups. On the other
hand, instead of analyzing the whole optical flow field, Chen
et al. [17] characterized crowd motion through a set of salient
points. This approach faces considerable complexity in
detection of objects, tracking trajectories, and recognizing
activities in dense crowds where the whole process is affected
by occlusions.
III. PROPOSED METHOD
Proposed method consists of four major modules: i.)ROI
Mask, ii) Boundary Detection, iii) Histogram Oriented Particle
Flow, iv) Non Linear one class SVM .The proposed method is
explored in Fig.1. Input video we have taken from public
datasets PEDS.
INPUT
VIDEO
HISTOGRAM
ORIENTED OF
PARTICLE
FLOW
FRAME
CONVERSION
BOUNDARY
DETECTION
&LABELING
GRAY SCALE
CONVERSION
ROI MASK
78
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
NON LINEAR
ONE CLASS
SVM
Fig .1 B
DETECTION OF
ABNORMAL
BEHAVIOUR
lock Diagram to detect the abnormal pedestrians in crowded environment
A. ROI Mask
The first step in our image processing is to separate
foreground objects from backgrounds in images. To this end,
we first apply a binary region-of-interest (ROI) mask to the
image or frame [18]. Note that, for detecting abnormal events,
this manual operation is reasonable as we often want to focus
on specific areas in cameras’ viewing fields.
A region of interest (ROI) is a portion of an image that we
want to filter or perform some other operation on. So we
define an ROI by creating a binary mask, which is a binary
image that is the same size as the image we want to process
with pixels that define the ROI set to 1 and all other pixels set
to 0[18]. We can define more than one ROI in an image.
The regions can be geographic in nature, such as polygons that
encompass contiguous pixels, or they can be defined by a
range of intensities.
B. Boundary Detection
Boundary detection is a fundamental task in computer
vision, with broad applicability in areas such as feature
extraction, object recognition and image segmentation. The
majority of papers on edge detection have focused on using
only low-level cues, such as pixel intensity or color. Recent
work has started exploring the problem of boundary detection
based on higher-level representations of the image, such as
motion, surface and depth cues, segmentation, as well as
category specific information. In this paper we propose a
general formulation for boundary detection that can be
applied, in principle, to the identification of any type of
boundaries, such as general edges from low-level static cues,
and occlusion boundaries from motion and depth cues. We
generalize the classical view of boundaries from sudden signal
changes on the original low-level image input, to a locally
linear (planar or step-wise) model on multiple layers of the
input, over a relatively large image region [19]. The layers can
be interpretations of the image at different levels of visual
processing, which could be low-level (e.g., color or grey level
intensity), mid-level (e.g., segmentation, optical flow),or highlevel (e.g., object category segmentation). We can summarize
our assumptions as follows:
1. A boundary separates different image regions, which in
the absence of noise are almost constant, at some level of
image interpretation or processing. For example, at the lowest
level, a region could have constant intensity. At a higher-level,
it could be a region delimiting an object category, in which
case the output of a category-specific classifier would be
constant.
2. For a given image, boundaries in one layer often
coincide, in terms of position and orientation, with boundaries
in other layers. For example, discontinuities in intensity are
typically correlated with discontinuities in optical flow,
texture or other cues. Moreover, the boundary that aligns
across multiple layers typically corresponds to the semantic
boundaries that interest humans.
C. Histogram Oriented Particle Flow Method
In corner feature trajectories or optical flow estimation, are
sufficient to generate predictive model, they do not address the
problem of groups crowd and their focus isn’t on a basis for
further midlevel analysis of events.[20] Analyzing human
crowds is becoming an important issue in video surveillance
and one challenging task is to detect group-level crowd due to
their non-rigid shapes nature. Particle flow method has the
ability to track crowd trajectories . Fundamental to the success
of any algorithms for recognizing group activities is the ability
to track individuals (or group of individuals) under crowded
conditions. However, such group-level crowd result in
occlusions and the goal of extract trajectories for each
individual may not be possible. The key advantage of particle
video approach is that it is both spatially dense and temporally
long-range. In contrast, feature tracking is long range but
spatially sparse and optical flow is dense but temporally shortrange.
D. Non-Linear One Class SVM
In this paper, abnormal events are detected by nonlinear
one-class SVM classification methods. In general, a non-linear
one-class SVM algorithm shows high performance results
based on learning normal behavior frames. The research in the
79
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
machine learning field focusing on improving the
effectiveness of pattern classification can be adapted to obtain
More accurate abnormal detection results. When some arts
of the image contain no motion during the training phase, the
one-class SVM algorithm is robust because it is based on the
global behavior of the frame. However, if the size of the block
is small, the SVM is not robust and can detect abnormal
situation when a movement occurs in this part. In abnormal
detection problems, it is supposed that the samples from a
positive class are obtainable [21]. The one-class SVM
framework is then suitable to the specificity of the abnormal
event detection where only normal scene data are available. In
machine learning, support vector machine (SVM) is initially
presented by Vapnik and Lerner , it is a method of statistical
learning theory that analyzes data and recognizes patterns,
used for classification and regression analysis[22]. By
adopting a kernel trick, which implicitly maps inputs into
high-dimensional feature space, SVM can effectively perform
Non- linear classification problems.
Fig.3(a)Starting Frame
Fig.3(b)Ending Frame
IV. EXPERIMENTAL RESULTS
A. Dataset
We had considered the two various inputs as video
sequences from PEDS datasets which is related to pedestrian
activities in crowd (like AVI or MPEG format).From that, we
evaluate the performance for ROI Mask, Bounding and
Labelling representations, Histogram Oriented Particle flow &
Non linear one class SVM Classification by simulations
conducted in MATLAB (version 2014).
Fig.4 (a) Starting Frame
Fig.4 (b) Ending Frame
C. Gray Scale Conversion
The RGB frame has been converted in to gray scale to
detect the pedestrians in an easier way.
Fig.5 Grayscale Conversion for the different view of data sets
Fig.2(a).PEDS(View_001) Dataset
Fig.2(b).PEDS(View_002) Dataset
B. Frame Conversion
Pedestrian videos from different view have been
converted to frames using MATLAB.
D. ROI Mask
Next to separate foreground objects from backgrounds, we
first apply binary ROI mask to an image. By applying the ROI
mask we can separate the particular crowded people by
80
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
selecting pixel values. The ROI mask and selected regions are
obtained by applying the mask are shown in Fig.6
of features for the people present in crowded area has been
done by the Boundary Representation and Labeling.
a) PEDS(View_001) Dataset
Fig.8(a)&(b) Boundary Representation & Labeling Where red Color
Shows that Boundary representation and Green color resembles that labeling
of pedestrians in crowded environment
Fig. 6 (a) ROI mask
(b) Selected portion by applying mask
b) PEDS(View_002) Dataset
Fig. 7 (c) ROI mask
E.
F. Histogram Oriented Particle Flow method
After the Boundary representation and labeling process,
the crowd flow has been estimated by using Histogram
Oriented Particle flow method. The Dense Motion of Crowd
has been shown by red color and least motion shown as black
color in below fig through particle position and Histogram
Representation in an two dimensional manner.
(d) Selected portion by applying mask
Boundary Representation and Labeling
In this paper, from ROI Mask the people in crowded area
has been identified .Extraction of contour effects & Selection
Fig.9 (a )HOPF Algorithm for PEDS(View_001)Dataset
81
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
College of Engineering & Technology) for allowing us to use
some of the published research results. We are also thankful to
the UG students of ECE, Velammal College of Engineering &
Technology for their valuable feedback. Finally, We are
indebted to Velammal College of Engineering & Technology
for encouraging our research work.
REFERENCES
Fig.9 (b)HOPF Algorithm for PEDS(View_002)Dataset
V. CONCLUSION
We have presented a new method for detecting abnormal
event detection of the global frame is proposed. The method
consists of two components: computing Histogram Oriented
Particle flow method, and applying a non-linear one-class
SVM for classification. The HOP feature descriptor, which is
obtained after applying ROI Mask and Boundary Detection for
fast implementation. The proposed detection algorithm has
been tested on several video datasets yielding successful
results in detecting abnormal events. The abnormal detection
may be applied on a region of interest or a specific tracked
object.To derive various features, first step in our processing is
to separate foreground objects from backgrounds using ROI
mask. In previous approaches, occlusion occurs so it is
difficult to detect an crowd behaviour. But these drawbacks
are overcomed by our method and it attains much lower
prediction errors than existing approaches. Further,We will
use the Non-Linear one class SVM to detect the abnormal
human activity from an normal one.
VI. ACKNOWLEDGMENT
Our Sincere thanks to Dr.S.Vasuki (Head of the
Electronics and Communication Department, Velammal
College of Engineering & Technology), we are grateful to
Assistant Prof. V.G.Janani (ECE Department, Velammal
[1] O. P. Popoola and K. Wang, “Video-based abnormal human behavior
recognition— A review,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev.,
vol. 42, no. 6, pp. 865–878, Nov. 2012.
[2] R. T. Collins et al., A System for Video Surveillance and Monitoring,
vol. 2. Pittsburgh, PA, USA: Carnegie Mellon Univ., 2000.
[3] J. Candamo, M. Shreve, D. B. Goldgof, D. B. Sapper,and
R. Kasturi, “Understanding transit scenes: A survey on human
behavior-recognition algorithms,” IEEE Trans. Intell. Transp. Syst.,
vol. 11, no. 1, pp. 206–224, Mar. 2010.
[4] P. Metro. (2013, Oct. 1). Autonomous Operator of Parisian Transports
[Online]. Available: http://www.ratp.fr/
[5] F. Ziliani et al., “Performance evaluation of event detection solutions:
The creds experience,” in Proc. IEEE Conf. AVSS, Sep. 2005,
pp. 201–206.
[6] G. Lavee, E. Rivlin, and M. Rudzsky, “Understanding video events:
survey of methods for automatic interpretation of semantic occurrences
in video,” Technion-Israel Inst. Technol., Haifa, Israel, Tech. Rep.
CIS-2009-06, 2009.
[7] G. Lavee, E. Rivlin, and M. Rudzsky, “Understanding video events:
A survey of methods for automatic interpretation of semantic occurrences
in video,” IEEE Trans. Syst. Man, Cybern. C, Appl. Rev., vol. 39,
no. 5, pp. 489–504, Sep. 2009.
[8] D. Kosmopoulos and S. P. Chatzis, “Robust visual behavior recognition,”
IEEE Signal Process. Mag., vol. 27, no. 5, pp. 34–45, Sep. 2010.
[9] Á. Utasi and L. Czúni, “Detection of unusual optical flow patterns by
multilevel hidden Markov models,” Opt. Eng., vol. 49, no. 1, p. 017201,
2010.
[10] T. Xiang and S. Gong, “Incremental and adaptive abnormal behaviour
detection,” Comput. Vis. Image Understand., vol. 111, no. 1, pp. 59–73,
2008.
[11] T. Xiang and S. Gong, “Video behaviour profiling and abnormality
detection without manual labelling,” in Proc. IEEE 10th ICCV, vol. 2.
Oct. 2005, pp. 1238–1245.
[12] T. S. Haines and T. Xiang, “Delta-dual hierarchical Dirichlet processes:
A pragmatic abnormal behaviour detector,” in Proc. IEEE ICCV,
Nov. 2011, pp. 2198–2205.
[13] J. Varadarajan and J.-M. Odobez, “Topic models for scene analysis
and abnormality detection,” in Proc. 12th ICCV Workshops, 2009,
pp. 1338–1345.
[14] S. K wak and H. Byun, “Detection of dominant flow and abnormal
surveillance video,” Opt. Eng., vol. 50, no. 2, pp. 027202-1– 027202- 8
.
[15] J. Kim and K. Grauman, “Observe locally, infer globally: A space-time
MRF for detecting abnormal activities with incremental updates,” in
Proc. IEEE Conf. CVPR, Jun. 2009, pp. 2921–2928.
[16] Y. Benezeth, P.-M. Jodoin, and V. Saligrama, “Abnormality detection
using low-level co-occurring events,” Pattern Recognit. Lett., vol. 32,
no. 3, pp. 423–431, 2011.
[17] A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, “Robust realtime
82
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
unusual event detection using multiple fixed-location monitors,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 3, pp. 555–560,
Mar. 2008.
[18] M. C. Casey, D. L. Hickman, A. Pavlou, and J. R. Sadler, “Smallscale
anomaly detection in panoramic imaging using neural models of
low-level vision,” Proc. SPIE, vol. 8042, pp. 80420X-1–80420X-11,
Jun. 2011.
[19] O. Boiman and M. Irani, “Detecting irregularities in images and in
video,” Int. J. Comput. Vis., vol. 74, no. 1, pp. 17–31, 2007.
[20] C. Bregler, “Learning and recognizing human dynamics in video
sequences,” in Proc. IEEE Conf. CVPR, Jun. 1997, pp. 568–574.
[21] T. Starner and A. Pentland, “Real-time American Sign Language
from video using hidden Markov models,” in Proc. Int. Symp.
Comput. Vis., Coral Gables, FL, USA, 1995.
[22] A. F. Bobick and J. W. Davis, “The recognition of human movement
using temporal templates,” IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 23, no. 3, pp. 257–267, Mar. 2001.
[23] Y. Chen, G. Liang, K. K. Lee, and Y. Xu, “Abnormal behavior detection
by multi-SVM-based Bayesian network,” in Proc. ICIA, Jul. 2007,
pp. 298–303.
[24] C. Schuldt, I. Laptev, and B. Caputo, “Recognizing human actions:
A local SVM approach,” in Proc. 17th ICPR, vol. 3. 2004, pp. 32–36.
[25] B. Yao and L. Fei-Fei, “Modeling mutual context of object and human
pose in human-object interaction activities,” in Proc. IEEE Conf.
Jun. 2010, pp. 17–24.
[26] N. Dalal, B. Triggs, and C. Schmid, “Human detection using oriented
histograms of flow and appearance,” in Proc. ECCV, 2006, pp. 428–441.
[27] N. Dalal, “Finding people in images and videos,” Ph.D. dissertation,
Dept. Sci. Environ., Inst. Nat. Polytech. de Grenoble-INPG,
Saint-Martin-d’Hères, France, 2006.
[28] I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, “Learning
realistic human actions from movies,” in Proc. IEEE Conf. CVPR,
Jun. 2008, pp. 1–8.
[29] T.Wang, H. Snoussi, and F. Smach, “Detection of visual abnormal
via one-class SVM,” in Proc. Int. Conf. Pattern Recognit. IPCV, vol. 1.
2012, pp. 113–119.
[30] T. Wang and H. Snoussi, “Histograms of optical flow orientation for
visual abnormal events detection,” in Proc. IEEE 9th Int. Conf. AVSS,
Sep. 2012, pp. 13–18.
[31] UMN, Minneapolis, MN, USA. (2006). Unusual Crowd Activity Dataset
of University of Minnesota, Department of Computer Science and
Engineering .
[32] PETS, Vellore, India. (2009). Performance Evaluation of Tracking
and Surveillance (PETS) 2009 Benchmark Data. Multisensor
Sequences Containing Different Crowd Activities [Online]. Available:
http://www.cvg.rdg.ac.uk/pets2009/a.html
[33] B. K. Horn and B. G. Schunck, “Determining optical flow,” Artif. Intell.,
vol. 17, no. 1, pp. 185–203, 1981.
[34 ] V. N. Vapnik and A. Lerner, “Pattern recognition using generalized
portrait method,” Autom. Remote Control, vol. 24, no. 6, pp. 774–780,
1963.
[35] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for
optimal margin classifiers,” in Proc. ACM 5th Annu. Workshop COLT,
Pittsburgh, PA, USA, Jul. 1992, pp. 144–152.
[36] C. Piciarelli, C. Micheloni, and G. L. Foresti, “Trajectory-based
event detection,” IEEE Trans. Circuits Syst. Video Technol., vol. 18,
no. 11, pp. 1544–1554, Nov. 2008.
[37] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector
Machines and Other Kernel-Based Learning Methods. Cambridge, U.K.:
Cambridge Univ. Press, 2000.
[38] B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and
R. C. Williamson, “Estimating the support of a high-dimensional
Neural Comput.”, vol. 13, no. 7, pp. 1443–1471, 2001.
[39 ] B. Schölkopf and A. J. Smola, Learning With Kernels: Support Vector
Machines, Regularization, Optimization and Beyond. Cambridge, MA,
USA: MIT Press, 2002.
[40] V. Vapnik, The Nature of Statistical Learning Theory. New York, NY,
USA: Springer-Verlag, 2000.
[41] V. N. Vapnik, Statistical Learning Theory. Hoboken, NJ, USA: Wiley,
1998.
[42] O. Tuzel, F. Porikli, and P. Meer, “A Bayesian approach to background
modeling,” in Proc. IEEE Comput. Soc. Conf. CVPR Workshops,
Jun. 2005, p. 58.
[43] F. Porikli and A. Yilmaz, “Object detection and tracking,” in Video
Analytics for Business Intelligence. New York, NY, USA: SpringerVerlag, 2012, pp. 3–41.
[44] R. Mehran, A. Oyama, and M. Shah, “Abnormal crowd behavior
detection using social force model,” in Proc. IEEE Conf. CVPR, Miami,
FL, USA, Jun. 2009, pp. 935–942.
[45] Y. Cong, J. Yuan, and J. Liu, “Sparse reconstruction cost for abnormal
event detection,” in Proc. IEEE CVPR, Colorado Springs, CO, USA,
Jun. 2011, pp. 3449–3456.
[46] Y. Shi, Y. Gao, and R. Wang, “Real-time abnormal event detection in
complicated scenes,” in Proc. 20th ICPR, Istanbul, Turkey, Aug. 2010,
pp. 3653–3656.
83
All Rights Reserved © 2015 IJARTET