A method of on-road vehicle detection based on comprehensive feature cascade of classifier

To detecting on-road vehicles rapidly and efficiently, we propose a robust method to improve the accuracy of on-road vehicle detection rate and reduce false alarm rate. Firstly, we have enhanced the feature expressive force by combining Haar-like feature with BRIEF feature. Some improvements have been done for achieving the robustness under the lighting and road conditions changes. Secondly, we have improved the performance of weak classier based on Gentle Adaboost algorithm. Experimental results show that the detection rate increased by 2.6% compared with the traditional cascade structure of classifier, and the false alarm rate reduced in some degrees.