Automatic Abnormal Behaviour Detection from videos
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Tummala. Yenosh, CH. Kavitha, M. Babu Rao
|DOI : 10.14445/22315381/IJETT-V30P255|
Tummala. Yenosh, CH. Kavitha, M. Babu Rao"Automatic Abnormal Behaviour Detection from videos", International Journal of Engineering Trends and Technology (IJETT), V30(6),294-299 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
This paper address the detection of anomalies in real time video surveillance applications such as ATM attacks, accidents, crime, etc. A new technique is proposed to detect anomalies using Joint Kernel Sparsity Model (JKSM) with Multi-Channel Kernel Fuzzy Correlogram (MKFC). Moving objects are segmented by using MKFC in the situations like occlusion, newly appearing or disappearing objects and JKSM is used to detect the anomalies involving in multiple objects. It extracts the features of normal/anomalous events, assign the label to features, and model the prelabeled features as linear combination in a training dictionary. The proposed technique is implemented on two different data sets and the result shows improved precision and recall over the existing technique.
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Video Anomaly, Joint Kernel Sparsity Model, Multi-Channel Kernel Fuzzy Correlogram.