Boosting算法和神经网络相结合的行人检测

In this paper, a fast pedestrian detection system by boosting neural network classifiers is built. The object to be detected is represented by a collection of blocks. For each block, the histogram of orientated gradients feature is extracted and a neural network classifier is built as weak hypothesis. Then these hypotheses are selected sequentially by Gentle Adaboost, and the cascade structure is used to speedup the detector. Compared to global linear SVM classifiers, the new method gets better performance on the INRIA pedestrian detection database at a much faster speed.