Vehicle Recognition Technology Based on Haar-Like Feature and AdaBoost

It is quite important for solving crimes and tracking the suspect to find the vehicle quickly and accurately from huge volume video records. By extracting Haar-like features and adopting AdaBoost algorithm to construct classifier, one can identify vehicle in surveillance video. Aiming at the high false alarm rate of the original algorithm, this paper proposes two improved methods: the one adopts the background difference algorithm to remove the interference of background, and the other utilizes the target object difference algorithm to achieve the second identification. Experimental results have shown that the proposed algorithms can reduce the false alarm rate and improve the detection rate. Moreover, the two algorithms have better detection results for surveillance videos in different traffic scenes.