Vehicle detection under varying poses using Conditional Random Fields

Traditional vision based vehicle detection methods are more successful in detecting front and rear vehicles. However, the problem of detecting vehicles under various poses still presents a great deal of difficulty. Pose variation leads to limit the use of vision based driver assistance systems. In this paper, we present a Conditional Random Fields (CRFs) based algorithm that can detect vehicles under various poses. We treat this problem in a different way. We extract textural properties from small image patches as well as colors. Then CRFs model is employed to incorporate the contextual information. Firstly, we classify these patches into vehicular surfaces or background surfaces. Then we use clustering algorithm to eliminate the false alarms and detect multiple vehicles. From the quantitative evaluation of the proposed methods, our algorithm can be used in many practical applications that do not need accurate segmentation of vehicles.