Title: Surveillance video summarisation by jointly applying moving object detection and tracking

Authors: Ying Chen; Bailing Zhang

Addresses: Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China ' Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China

Abstract: With the growth of massive storage of surveillance video data, it has become imperative to design efficient tools for video content browsing and management. This paper describes an integrative approach for surveillance video summarisation that jointly apply moving object detection and tracking. In the proposed scheme, moving objects are first detected and tracked. The static summarisation is generated to contain some key frames which provide details of the moving objects. The main advantages of our approach include the preservation of important information and economic computational cost. The high performance background modelling with Gaussian mixture model, together with the multi-scale morphological processing, brings together a highly accurate moving object detection tool. The proposed matching criterions for Kalman filtering enhances the tracking accuracy. We experimented with highway surveillance videos and outdoor surveillance videos, demonstrating satisfactory performances.

Keywords: video summarisation; object tracking; object detection; key frames; trajectories; surveillance video; moving objects; video content browsing; video content management; background modelling; Gaussian mixture model; multi-scale morphological processing; Kalman filter; tracking accuracy.

DOI: 10.1504/IJCVR.2014.062936

International Journal of Computational Vision and Robotics, 2014 Vol.4 No.3, pp.212 - 234

Received: 30 Apr 2013
Accepted: 21 Nov 2013

Published online: 25 Jun 2014 *

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