Efficient feature fusion network based on center and scale prediction for pedestrian detection

Center and scale prediction (CSP) is an anchor-free pedestrian detector with good performance. However, there are lots of parameters in the detector, which seriously limits the speed. In this paper, a new network is designed for the improvement of the detector speed, which contains less parameters, named Feature Fusion: Center and Scale Prediction (F-CSP). F-CSP fuses multi-scale feature maps with two efficient feature fusion networks: Feature Pyramid Networks (FPN) and Balanced Feature Pyramid (BFP). Specifically, FPN is used to reduce the channel of feature maps, and BFP is used to fuse multiple feature maps into a single one. This way, the proposed detector achieves competitive accuracy and higher speed on the challenging pedestrian detection benchmark. The performance of F-CSP is demonstrated on the Caltech dataset. Compared with CSP, under the premise of ensuring accuracy, the speed is increased from 45.1 to 32.9 ms/img.