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

arXiv: https://arxiv.org/abs/2001.09252 · HTML: https://mlsanity.com/paper/2001.09252/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Weakly Aligned Feature Fusion for Multimodal Object Detection](https://mlsanity.com/paper/2204.09848/) (2022)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [MDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos](https://mlsanity.com/paper/2303.14395/) (2023)
- [You Better Look Twice: a new perspective for designing accurate detectors with reduced computations](https://mlsanity.com/paper/2107.10050/) (2021)

## Abstract (arXiv)

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 (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 (HO) test set.