Pedestrian detection based on spatial attention module for outdoor video surveillance

Pedestrian detection remains challenging because of hard instances, such as illumination change, various occlusion, and special appearance, etc. The current methods to detect these hard examples depend on complicate manual designs or additional annotations. We observe that the spatial information of pedestrians can be obtained through motion information, which enlightens us to utilize this spatial information to guide effective training of detectors. In this paper, we introduce the Spatial Attention Module, which guides Convolutional Neural Networks (CNNs) to focus on potential pedestrian positions indicated by hierarchical unsupervised guidance information, including motion information and static information. The experimental results on two datasets demonstrate that the proposed method outperforms the state-of-the-art and can capture hard examples, which are missed by the baseline.