A SSD-based Crowded Pedestrian Detection Method

Abstract

Pedestrian detection has become a significant research topic in the field of computer vision. The performance of existing methods based on deep learning is not so good in pedestrian detection for complex background. Considering the problem of pedestrian detection in complex scenes with small and crowded objects, we propose a SSD-based crowded pedestrian detection method in this paper. Firstly, we increase density of default boxes on the horizontal direction by setting an offset, which can effectively eliminate the influence of missing matching default boxes and separate a person from the crowd much easier. So our detector is more suitable for complex scenes. Secondly, SSD is designed for general object detection, thus it is unfit for pedestrian detection because of the large aspect ratio of pedestrians. Therefore, we adopt abnormal 5*1 convolutional kernels instead of the standard 3*3 ones in order to adapt to pedestrian detection. Finally, we present experimental results on public benchmark datasets including Caltech dataset and INRIA dataset, which indicate that our method has better performance for pedestrian detection.