People detection in crowded scenes via regional based convolutional network
The detection of people in crowded scenes is a challenging task owing to both the severe occlusion among people and various changes in posture. We propose a regional based convolutional network to address the tasks of people detection in the crowded scenes. Unlike the traditional methods, we propose an end to end framework that uses the convolutional network for feature representation, which generates candidate proposals online and performs the location optimization simultaneously. By separately detecting the heads of people, our system gets rid of the problems caused by occlusion and achieves robust detection results in the case that no matter whether the head is towards or backwards to the camera. Experimental results show that it greatly outperforms traditional methods.
2016 IEEE 13th International Conference on Signal Processing (ICSP)