FE-CSP: a fast and efficient pedestrian detector with center and scale prediction

There are still many pressing problems in pedestrian detection, such as difficulty in detection due to severe pedestrian occlusion, difficulty in detecting small objects and low detection speed. In this paper, we propose A Fast and Efficient Pedestrian Detector with Center and Scale Prediction (FE-CSP). We combine channel attention with spatial attention, replace the traditional convolution with deformable convolution, and embed the backbone network to propose CSANet (Channel and Spatial Attention Network), which efficiently extracts the semantic features of the object, and then propose a feature pyramid network to replace the traditional concatenation to perform multi-scale feature detection, which effectively improves the detection speed. By conducting experiments on CityPersons, our method achieves 10.1%, 13.7% and 47.4% MR-2 at a speed of 0.21 s/img on the reasonable setting, small setting and heavy setting, respectively. On Caltech, our method achieves 5.2% MR-2 at a speed of 0.06 s/img on the Reasonable setting, further demonstrating the superiority and generalization ability of the proposed method.