Robust Vehicle Edge Detection by Cross Filter Method

In visual surveillance, vehicle tracking and identification is very popular and applied in many applications such as traffic incident detection, traffic control and management. Edge detection is the key to the success of vehicle tracking and identification. Edge detection is to identify edge locations or geometrical shape changes in term of pixel value along a boundary of two regions in an image. This paper aims to investigate different edge detection methods and introduce a Cross Filter (CF) method, with a two-phase filtering approach, for vehicle images in a given database. First, four classical edge detectors namely the Canny detector, Prewitt detector, Roberts detector and Sobel detector are tested on the vehicle images. The Canny detected image is found to offer the best performance in Phase 1. In Phase 2, the robust CF, based on a spatial relationship of intensity change on edges, is applied on the Canny detected image as a second filtering process. Visual and numerical comparisons among the classical edge detectors and CF detector are also given. The average DSR of the proposed CF method on 10 vehicle images is 95.57%.