Research on Vehicle Detection and Tracking Algorithm for Intelligent Driving

To develop the vehicle detection function for preceding car, this paper chooses the method based on machine vision learning for vehicle detection. The LBP and Haar features of cars are used as descriptors, and the positive and negative samples of the vehicle are trained by AdaBoost network, to achieve the detection network. The preceding car video is detected by the training network. Then, Kalman filter technology is introduced to solve the problem of CamShift which is prone to heel-and-miss when the moving state of the target changes. At the same time, the improved Mixture Gauss model and the designed tracking matrix list are used to realize the full automatic multi-target tracking based on CamShift algorithm. The simulation results show that the proposed scheme has fast detection speed, low tracking time complexity and good effect, which also has good comprehensive performance compared with similar algorithms.