A Single Camera-based Vehicle Detection And Tracking

The research content of this paper is vehicle detection and tracking based on a single camera. It is mainly to detect and track front vehicles. In general, vehicle detection is divided into two steps. The first step is to find vehicle candidate regions; the second step is to verify the vehicle candidate regions. In this paper, we firstly analyze the front vehicle's features in detail, and then we fuse vehicle's multiple features to detect a vehicle. The fused features include: shadow underneath the vehicle, texture, shape and gray-scale symmetry. According to find the shadow underneath a vehicle, we can get some ROI(Region Of Interesting), and then we use entropy to remove some disturbing regions which lack of entropy. The left regions are regarded as vehicle candidate regions. After above process, we finish the first step of vehicle detection. As model based method relies too much on model, it can not detect vehicles when model matches unsuccessfully. Another problem is that it is very difficult to establish a precise model for a vehicle. So we do not use model based method to detect a vehicle. We synchronously fuse vehicle's shape and gray-scale symmetry features to verify vehicle candidate regions. After above process, we finish the second step of vehicle detection. So far, vehicle detection has finished after these two steps. Our experiment results show that vehicle detection based on fusion of multiple features proposed in this paper can detect front vehicles with different shape, color, size, distance and gesture. This method is affected little by light condition, and can detect vehicles to some extent when camera shocks. It also can detect vehicles under different complex road environments. The vehicle detection method proposed in this paper is an effective method.In order to improve the robustness of vehicle detection and reduce time consuming, it always needs to track the detected vehicles after vehicle detection step. In vehicle tracking step, we firstly use model based vehicle tracking method and kalman filter based vehicle tracking method to track the detected vehicles. The experiment results of these two methods are not good. Model based vehicle tracking method will cause tracking drift, and can not deal with occlusion; Kalman filter based vehicle tracking method generally assumes that the motion of vehicle is uniformly linear motion or uniformly accelerated linear motion. This assumption is not appropriate for the actual movement of vehicles, and there are still problems about initializing the parameters of kalman filter. For the above reasons, we propose a vehicle tracking method called vehicle tracking based on fusion of model and gray-scale symmetry. The experiment results show that the proposed method can stably track front vehicles with long distance. It can adapt the condition that light changes drastically, and can deal with occlusion. Vehicle tracking based on fusion of model and gray-scale symmetry is an effective vehicle tracking method.