Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning

Pedestrian detection is a very important task in intelligent transportation system. State-of-the-art detectors work well on non-occluded pedestrians, but they are still far from satisfactory for heavily occluded ones. Recently, to deal with occlusion problems, the popular two-stage approaches are to build a two-branch architecture with the help of additional visible body annotations. However, these methods still have disadvantages. Either the two branches only use score-level fusion, which cannot guarantee the detectors to learn more robust pedestrian features. Or they only focus on the features of visible part via the attention mechanisms. However, the visible body features of heavily occluded pedestrians are only concentrated in a relatively small area, which may easily lead to missed detections. To alleviate the above issues, we propose a novel Distribution-based Mutual-Supervised Feature Learning Network (DMSFLN), to better deal with occluded pedestrian detection. The key DMSFL module in our network is to learn more discriminative feature representations of pedestrians by minimizing the similarity loss between feature distributions of full body and visible body, which has two advantages: enhancing the feature representations of occluded pedestrians and reducing the intra-class variance in pedestrians. To facilitate the DMSFL module, we also propose a novel two-branch network architecture, which is trained in a mutual-supervised way with both full body and visible body annotations respectively. Extensive experiments are conducted on four challenging pedestrian datasets: Caltech, CityPersons, CrowdHuman and CUHK occlusion. Our approach achieves superior performance compared to other state-of-the-art methods, especially on heavy occlusion subsets.