# Relational Learning for Joint Head and Human Detection

arXiv: https://arxiv.org/abs/1909.10674 · HTML: https://mlsanity.com/paper/1909.10674/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [Progressive End-to-End Object Detection in Crowded Scenes](https://mlsanity.com/paper/2203.07669/) (2022)
- [VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision](https://mlsanity.com/paper/2304.03135/) (2023)
- [Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait](https://mlsanity.com/paper/2505.04616/) (2025)
- [Variational Pedestrian Detection](https://mlsanity.com/paper/2104.12389/) (2021)
- [NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection](https://mlsanity.com/paper/2106.02426/) (2021)
- [Body-Part Joint Detection and Association via Extended Object Representation](https://mlsanity.com/paper/2212.07652/) (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)
- [PBADet: A One-Stage Anchor-Free Approach for Part-Body Association](https://mlsanity.com/paper/2402.07814/) (2024)
- [Density-based Object Detection in Crowded Scenes](https://mlsanity.com/paper/2504.09819/) (2025)
- [Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection](https://mlsanity.com/paper/2103.10091/) (2021)
- [MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings](https://mlsanity.com/paper/2110.03302/) (2021)
- [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)

## Abstract (arXiv)

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of human detector frequently drops dramatically in crowd scenes. To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. Moreover, we design a head-body relationship discriminating module to perform relational learning between heads and human bodies, and leverage this learned relationship to regain the suppressed human detections and reduce head false positives. To verify the effectiveness of the proposed method, we annotate head bounding boxes of the CityPersons and Caltech-USA datasets, and conduct extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets. As a consequence, the proposed JointDet detector achieves state-of-the-art performance on these three benchmarks. To facilitate further studies on the head and human detection problem, all new annotations, source codes and trained models will be public.