Vehicle detection based on semantic component analysis

Vehicle detection is a hot topic in trafic monitoring applications. Though many researchers has done a lot work towards this direction, the detection in occluded conditions is rarely explored and it still remains a challenge. In this work, we focus on the occlusion problem in vehicle detection and propose a novel method based on semantic component analysis and scale consideration. Two contributions are claimed in this procedure: 1) Tackling vehicle detection by semantic component detection and synthesis. 2) Addressing the scale variation of vehicles by simple yet effective standard component definition. The experimental results on two typical surveillance videos show that the proposed method can effectively detect the vehicles in the crowded trafic conditions with occlusion.