PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection

• URL: http://arxiv.org/abs/2001.09252v2
• Date: Tue, 10 Mar 2020 13:19:15 GMT
• Title: PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection
• Authors: Jin Xie and Yanwei Pang and Hisham Cholakkal and Rao Muhammad Anwer and Fahad Shahbaz Khan and Ling Shao
• Abstract summary: This paper introduces a novel approach, termed as PSC-Net, for occluded pedestrian detection. PSC-Net captures both inter and intra-part co-occurrence information of different pedestrian body parts through a Graph Convolutional Network (GCN) 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.
• Score: 144.19392893747582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting pedestrians, especially under heavy occlusions, 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 two challenging datasets: CityPersons and Caltech datasets. The proposed PSC-Net achieves state-of-the-art detection performance on both. On the heavy occluded (\textbf{HO}) set of CityPerosns test set, our PSC-Net obtains an absolute gain of 4.0\% in terms of log-average miss rate over the state-of-the-art with same backbone, input scale and without using additional VBB supervision. Further, PSC-Net improves the state-of-the-art from 37.9 to 34.8 in terms of log-average miss rate on Caltech (\textbf{HO}) test set.

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