Head detection based on convolutional neural network with multi-stage weighted feature

Human head detection is an important means of pedestrian detection and counting. By now, head detection is mainly based on outline, color and template which have low recognition rate and error tolerance. Recently, deep learning has become a research hotspot in the field of pattern recognition. As a model of deep learning, convolutional neural network (CNN) performs well in the areas of image recognition and speech analysis. In this paper, a new method based on CNN was proposed. This method uses a few new twists, such as multi-stage weighted feature and connections that skip layers to integrate global shape information and local motif information. The experimental results show that the proposed method performs a higher accuracy on head detection compared with the traditional ones´.