A cascade classifier using Adaboost algorithm and support vector machine for pedestrian detection
Wen-Chang Cheng, Ding-Mao Jhan · 2011
In this paper, we improve cascade-Adaboost classifier and propose a cascade-Adaboost-SVM classifier. It is combined with Adaboost and SVM and real-time pedestrian detection system with a single camera. We capture the pedestrian candidate areas with a window of fixed size, conduct feature extraction to candidate areas and mobile images with Haar-like rectangle feature calculation and then, complete pedestrian by using the proposed cascade-Adaboost-SVM classifier. As this cascade-Adaboost-SVM classifier can adjust numbers of cascade classifiers adaptively, it can construct cascade classifiers effectively based on training set. Finally, we complete the pedestrian detection experiment with the database of captured samples and PETs database. The experimental result shows that the cascade classifier proposed by us can get better performance than cascade-Adaboost classifier and its accuracy can reach 99.5% and the false alarm rate is less than 1e-5.