Title: Effective scene change detection in complex environments

Authors: Hui Fuang Ng; Chee Yang Chin

Addresses: Faculty of Information Communication and Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Perak, Malaysia ' Faculty of Information Communication and Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Perak, Malaysia

Abstract: One of the fundamental operations in computer vision applications is change detection, in which moving foreground objects are segmented from a static background. A common approach for change detection is the comparison of an image frame with the stored background model using a matching algorithm, a process known as background subtraction. However, such techniques fail in environments with dynamic backgrounds, illumination changes, or shadow and camera jitters. This study focuses on effectively detecting scene changes in complex environments. To this end, we proposed a new colour descriptor named local colour difference pattern (LCDP) that is insusceptible to shadow and is able to capture both colour and texture features at a pixel location. Furthermore, a scene change detection framework was proposed to handle dynamic scenes based on sample consensus that integrates LCDP and a novel spatial model fusion mechanism. Experiments using the CDnet benchmark dataset demonstrated the effectiveness of the proposed approach to change detection in complex environments.

Keywords: change detection; background subtraction; moving object segmentation; foreground segmentation; local descriptor; video signal processing; CDnet.

DOI: 10.1504/IJCVR.2019.099441

International Journal of Computational Vision and Robotics, 2019 Vol.9 No.3, pp.310 - 328

Received: 07 Feb 2018
Accepted: 21 Jul 2018
Published online: 02 May 2019 *

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