Moving target detection based on the properties of corners

In video surveillance, there are a variety of random disturbances on moving target detection, such as trees sway, camera shake. In order to eliminate these disturbances, this paper presents a novel moving object detection algorithm based on the properties of corner points. Firstly, this paper uses Harris algorithm to detect corner on the video image, and then proposes a novel indicator, Inter-frame Regional Corners Difference, to select the candidate grids of foreground. In this way, the static and slight shaking background is recognized. Secondly, this paper makes use of Horn and Schunck's optical flow algorithm to build the optical flow field of grids that are the candidates of foreground, and extracts the moving target by some velocity constraint of the horizontal and vertical direction. In virtue of the different dynamic properties of background and moving target, the dynamic background can be eliminated. The experimental results show that our algorithm can accurately extract moving target and can meet the needs of real-time processing with strong anti-jamming.