Abstract
Detection of abnormal human actions in the crowd has become a critical problem in video surveillance applications like terrorist attacks. This paper proposes a real-time video surveillance system which is capable of classifying normal and abnormal actions of individuals in a crowd. The abnormal actions of human such as running, jumping, waving hand, bending, walking and fighting with each other in a crowded environment are considered. In this paper, Relevance Vector Machine (RVM) is used to classify the abnormal actions of an individual in the crowd based on the results obtained from projection and skeletonization methods. Experimental results on benchmark datasets demonstrate that the proposed system is robust and efficient. A comparative study of classification accuracy between Relevance Vector Machine and Support Vector Machine (SVM) classification is also presented.
Highlights
Security of citizens in public places such as Hotels, Markets, Airports, and Train stations is increasingly becoming a crucial issue
A number of video surveillance systems for multiple people detection and tracking in a crowded environment have been reported in literature [1,2,3]
The individual is to be identified for further analysis leading to action classification
Summary
Security of citizens in public places such as Hotels, Markets, Airports, and Train stations is increasingly becoming a crucial issue. Turaga et al have summarized the approaches that have been pursued over the last 20 years to address the problem of activity recognition They have discussed the problem at two levels of complexity: “actions” and “activities.” “Actions” are characterized by simple motion patterns typically executed by an individual whereas “activities” involve coordinated actions among a group [9]. If there exists more than one blob, but with connectivity, there is a likelihood to be considered as single entity This results in the identification of a group as “individual.” This makes recognition of individual’s action in a crowd more difficult. The blob is projected to head and ground plane from the camera view point leading to intersected area in world coordinates.
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