PSC-Net: learning part spatial co-occurrence for occluded pedestrian detection

Detecting pedestrians, especially under heavy occlusion, is a challenging computer vision problem with numerous real-world applications. This paper introduces a novel approach, termed as PSC-Net, for occluded pedestrian detection. The proposed PSC-Net contains a dedicated module that is designed to explicitly capture both inter and intra-part co-occurrence information of different pedestrian body parts through a graph convolutional network (GCN). Both inter and intra-part co-occurrence information contribute towards improving the feature representation for handling varying level of occlusions, ranging from partial to severe occlusions. Our PSC-Net exploits the topological structure of pedestrian and does not require part-based annotations or additional visible bounding-box (VBB) information to learn part spatial co-occurrence. Comprehensive experiments are performed on three challenging datasets: CityPersons, Caltech, and CrowdHuman datasets. Particularly, in terms of log-average miss rates and with the same backbone and input scale as those of the state-of-the-art MGAN, the proposed PSC-Net achieves absolute gains of 4.0% and 3.4% over MGAN on the heavy occlusion subsets of CityPersons and Caltech test sets, respectively.