Abnormal event detection via multi-instance dictionary learning

Authors:
Jing Huo;Yang Gao;Wanqi Yang;Hujun Yin
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, China;State Key Laboratory for Novel Software Technology, Nanjing University, China;State Key Laboratory for Novel Software Technology, Nanjing University, China;School of Electrical and Electronic Engineering, The University of Manchester, UK
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

In this paper, we present a method for detecting abnormal events in videos. In the proposed method, we define an event containing several sub-events. Sub-events can be viewed as instances and an event as a bag of instances in the multi-instance learning formulation. Given labeled events but with the labels of sub-events unknown, the proposed method is able to learn a dictionary together with a classification function. The dictionary is capable of generating discriminant sparse codes of sub-events while the classification function is able to classify an event. This method is suited for scenarios where the label of a sub-event is ambiguous, while the label of a set of sub-events is definite and is easy to obtain. Once the sparse codes of sub-events are generated, the classification of an event is carried out according to the result given by the classification function. An efficient optimization procedure of the proposed method is presented. Experiments show that the method is able to detect abnormal events with comparable or improved accuracy compared with other methods.