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Abstract
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Article Information:
Detecting Abnormal Behaviors in Crowded Scenes
Oluwatoyin P. Popoola and2Hui Ma
Corresponding Author: Hui Ma
Submitted: May 08, 2012
Accepted: May 29, 2012
Published: October 15, 2012 |
Abstract:
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Situational awareness is a basic function of the human visual system, which is attracting a lot of
research attention in machine vision and related research communities. There is an increasing demand for
smarter video surveillance of public and private space using intelligent vision systems which can
distinguish what is semantically meaningful to the human observer as ‘normal’ and ‘abnormal’ behaviors.
In this study we propose a novel robust behavior descriptor for encoding the intrinsic local and global
behavior signatures in crowded scenes. Crowd scenes transitioning from normal to abnormal behaviors
such as “rush”, “scatter” and “herding” were modeled and detected. The descriptor uses features that
encode both local and global signatures of crowd interactions. Bayesian topic modeling is used to capture
the intrinsic structure of atomic activity in the video frames and used to detect the transition from normal to
abnormal behavior. Experimental results and analysis of the proposed framework on two publicly available
crowd behavior datasets show the effectiveness of this method compared to other methods for anomaly
detection in crowds with a very good detection accuracy rates.
Key words: Abnormal behavior detection, intelligent video surveillance, situation awareness, , , ,
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Cite this Reference:
Oluwatoyin P. Popoola and2Hui Ma, . Detecting Abnormal Behaviors in Crowded Scenes. Research Journal of Applied Sciences, Engineering and Technology, (20): 4171-4177.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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