# PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

arXiv: https://arxiv.org/abs/1909.06826 · HTML: https://mlsanity.com/paper/1909.06826/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2019.

## Built on by (most cited first, 30 of 30)

- [Progressive End-to-End Object Detection in Crowded Scenes](https://mlsanity.com/paper/2203.07669/) (2022)
- [Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2504.09228/) (2025)
- [VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision](https://mlsanity.com/paper/2304.03135/) (2023)
- [Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation](https://mlsanity.com/paper/2201.01297/) (2022)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes](https://mlsanity.com/paper/2407.11464/) (2024)
- [Variational Pedestrian Detection](https://mlsanity.com/paper/2104.12389/) (2021)
- [Integrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection](https://mlsanity.com/paper/2311.01025/) (2023)
- [Beta R-CNN: Looking into Pedestrian Detection from Another Perspective](https://mlsanity.com/paper/2210.12758/) (2022)
- [NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection](https://mlsanity.com/paper/2106.02426/) (2021)
- [E-Scooter Rider Detection and Classification in Dense Urban Environments](https://mlsanity.com/paper/2205.10184/) (2022)
- [Body-Part Joint Detection and Association via Extended Object Representation](https://mlsanity.com/paper/2212.07652/) (2022)
- [Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification](https://mlsanity.com/paper/2212.09498/) (2022)
- [Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection](https://mlsanity.com/paper/2305.10801/) (2023)
- [V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection](https://mlsanity.com/paper/2104.03106/) (2021)
- [The Impact of Partial Occlusion on Pedestrian Detectability](https://mlsanity.com/paper/2205.04812/) (2022)
- [Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads](https://mlsanity.com/paper/2304.07705/) (2023)
- [BPJDet: Extended Object Representation for Generic Body-Part Joint Detection](https://mlsanity.com/paper/2304.10765/) (2023)
- [Data Augmentation in Human-Centric Vision](https://mlsanity.com/paper/2403.08650/) (2024)
- [You Better Look Twice: a new perspective for designing accurate detectors with reduced computations](https://mlsanity.com/paper/2107.10050/) (2021)
- [An Objective Method for Pedestrian Occlusion Level Classification](https://mlsanity.com/paper/2205.05412/) (2022)
- [PBADet: A One-Stage Anchor-Free Approach for Part-Body Association](https://mlsanity.com/paper/2402.07814/) (2024)
- [Beyond Overall Accuracy: Pose- and Occlusion-driven Fairness Analysis in Pedestrian Detection for Autonomous Driving](https://mlsanity.com/paper/2509.26166/) (2025)
- [Layer-Guided UAV Tracking: Enhancing Efficiency and Occlusion Robustness](https://mlsanity.com/paper/2602.13636/) (2026)
- [Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection](https://mlsanity.com/paper/2103.10091/) (2021)
- [Background Invariance Testing According to Semantic Proximity](https://mlsanity.com/paper/2208.09286/) (2022)
- [Ranking-based Adaptive Query Generation for DETRs in Crowded Pedestrian Detection](https://mlsanity.com/paper/2310.15725/) (2023)
- [DynamicTrack: Advancing Gigapixel Tracking in Crowded Scenes](https://mlsanity.com/paper/2407.18637/) (2024)
- [Mean Height Aided Post-Processing for Pedestrian Detection](https://mlsanity.com/paper/2408.13646/) (2024)
- [From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety](https://mlsanity.com/paper/2510.03314/) (2025)

## Abstract (arXiv)

Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling ability to existing region-based detection networks without bringing extra computations in the inference stage. Specifically, we design a mask-guided module to leverage the head information to enhance the feature representation learning of the backbone network. Moreover, we develop a strict classification criterion by improving the quality of positive samples during training to eliminate common false positives of pedestrian detection in crowded scenes. Besides, we present an occlusion-simulated data augmentation to enrich the pattern and quantity of occlusion samples to improve the occlusion robustness. As a consequent, we achieve state-of-the-art results on three pedestrian detection datasets including CityPersons, Caltech-USA and CrowdHuman. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. The proposed dataset, source codes and trained models will be released.