Robust Salient Motion Detection with Complex Background for Real-Time Video Surveillance

Abstract

Moving object detection is very important for video surveillance. In many environments, motion maybe either interesting (salient) motion (e.g., a person) or uninteresting motion (e.g., swaying branches.) In this paper, we propose a new real-time algorithm to detect salient motion in complex environments by combining temporal difference imaging and a temporal filtered motion field. We assume that the object with salient motion moves in a consistent direction for a period of time. No prior knowledge about object size and shape is necessary. Compared to background subtraction methods, our method does NOT need to learn the background model from hundreds of images and can handle quick image variations; e.g., a light being turned on or off. The average speed of our method is about 50fps on images at size 160x120 in 1GB Pentium III machines. The effectiveness of the proposed algorithm to robust detect salient motion is demonstrated for a variety of real environments with distracting motions such as lighting changes, swaying branches, rippling water, waterfall, and fountains.