Pedestrian detection based on deep learning model
Hailong Li, Zhendong Wu, Jianwu Zhang · 2016
Pedestrian detection remains an important task in the theory research and practical application of objects detection. Traditional pedestrian detection algorithms require experts design features to describe the pedestrian characteristics and combine with the classifiers. In recent years, deep learning and especially Convolutional Neural Networks (CNN) have made great success on image and audio, which is the important component of deep learning. Artificial designed methods of feature extracting has an imperfect description of pedestrian in the complex background. In this paper, we propose a pedestrian detection method based on deep convolutional neural network with multi-layers. It can make full use of the advantages of deep convolutional neural network and extract features from the database of pedestrian detection. At the stage of region proposal, to solve the problem of too much redundant windows generated by traditional methods, we use the edge boxes algorithm instead of sliding window algorithm to extract windows. At last, we get a smaller number of windows with high-quality, which is of great importance for the subsequent classification task. At the end of the paper, we carried out multi-sets of comparison experiments in this system. Experiments show that the pedestrian detection system based on deep learning outperforms the traditional methods based on both handcrafted and learned features.