Total Variation Regularized RPCA for Irregularly Moving Object Detection Under Dynamic Background

Abstract—Moving object detection is one of the most fundamental tasks in computer vision. Many classic and contemporary algorithms work well under the assumption that backgrounds are stationary and movements are continuous, but degrade sharply when they are used in a real detection system, mainly due to: 1) the dynamic background (e.g., swaying trees, water ripples and fountains in real scenarios, as well as raindrops and snowflakes in bad weather) and 2) the irregular object movement (like lingering objects). This paper presents a unified framework for addressing the difficulties mentioned above, especially the one caused by irregular object movement. This framework separates dynamic background from moving objects using the spatial continuity of foreground, and detects lingering objects using the temporal continuity of foreground. The proposed framework assumes that the dynamic background is sparser than the moving foreground that has smooth boundary and trajectory. We regard the...

• Type: Journal
• Year: 2016
• Where: TCYB
• Authors: Xiaochun Cao, Liang Yang, Xiaojie Guo
• Keywords: TCYB 2016