Effective scene change detection in complex environments. (16th April 2019)

• Record Type: Journal Article
• Authors: Ng, Hui Fuang; Chin, Chee Yang
• Is Part Of: International journal of computational vision and robotics. Volume 9:Number 3(2019)
• Page Start: 310
• Page End: 328
• Publication Date: 2019-04-16
• Subjects: change detection -- background subtraction -- moving object segmentation -- foreground segmentation -- local descriptor -- video signal processing -- CDnet; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

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.