Multi-scale pedestrian detection based on self-attention and adaptively spatial feature fusion

Pedestrian detection is a classic problem in computer vision, which has an essential impact on the safety of urban autonomous driving. Although significant improvement has been made in pedestrian detection recently, small-scale pedestrian detection is still challenging. To effectively tackle this issue, a multi-scale pedestrian detector based on self-attention mechanism and adaptive spatial feature fusion is proposed in this paper. In order to better extract global information, the spatial attention mechanism asymmetric pyramid non-local block (APNB) module is applied. To achieve scale-invariance detection, multiple detection branches are designed, which include a high-resolution detection branch and a low-resolution detection branch. In integrating multi-scale features, the adaptively spatial feature fusion (ASFF) method is employed, which can solve the problem of feature inconsistency across different scales. Experimental results show that the proposed method obtains competitive performance on Caltech and CityPersons datasets.