Title: Anomalies detection from video surveillance using support vector trained deep neural network classifier
Authors: S. Giriprasad; S. Mohan; S. Gokul
Addresses: Department of Electronics and Communication Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore 641 109, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore 641 109, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641 109, India
Abstract: Intelligent video surveillance plays a crucial role in various applications for detecting the abnormal activities. The surveillance system uses many significant technologies for detecting the anomalies in different applications but it fails to manage the accuracy while detecting the anomalies from huge crowd. This paper introduces an effective image processing technology-based classifier for recognising and detecting the abnormality from the crowd effectively. Initially, the videos are captured using the surveillance camera, and the background has been subtracted by the robust background principal analysis method. After extracting the background from the image, the different principal bow sift descriptors are extracted. Subsequently the similar descriptors are grouped using the bee-based collaborative filtering approach. Finally, the anomaly classification is done by support vector machine training-based deep neural networks. Then the excellence of the system is evaluated by using the implementation results and the obtained results are compared with the traditional classifiers.
Keywords: intelligent video surveillance; robust background principal analysis; principal bow sift descriptors; bee-based collaborative filtering approach; support vector machine training-based deep neural network.
International Journal of Heavy Vehicle Systems, 2018 Vol.25 No.3/4, pp.286 - 307
Received: 27 Apr 2017
Accepted: 27 Jun 2017
Published online: 24 Sep 2018 *