# Attribute-aware Pedestrian Detection in a Crowd

arXiv: https://arxiv.org/abs/1910.09188 · HTML: https://mlsanity.com/paper/1910.09188/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 20 of 20)

- [Oriented RepPoints for Aerial Object Detection](https://mlsanity.com/paper/2105.11111/) (2021)
- [Focal Inverse Distance Transform Maps for Crowd Localization](https://mlsanity.com/paper/2102.07925/) (2021)
- [SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm](https://mlsanity.com/paper/2312.01640/) (2023)
- [Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2304.10091/) (2023)
- [Integrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection](https://mlsanity.com/paper/2311.01025/) (2023)
- [An Empirical Study of Mamba-based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2407.10374/) (2024)
- [Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond](https://mlsanity.com/paper/2201.03176/) (2022)
- [F2DNet: Fast Focal Detection Network for Pedestrian Detection](https://mlsanity.com/paper/2203.02331/) (2022)
- [Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2404.17929/) (2024)
- [Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors](https://mlsanity.com/paper/2309.08771/) (2023)
- [Robust Pedestrian Detection via Constructing Versatile Pedestrian Knowledge Bank](https://mlsanity.com/paper/2404.19299/) (2024)
- [VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models](https://mlsanity.com/paper/2408.13031/) (2024)
- [Pedestrian Attribute Recognition via Hierarchical Cross-Modality HyperGraph Learning](https://mlsanity.com/paper/2509.22331/) (2025)
- [You Better Look Twice: a new perspective for designing accurate detectors with reduced computations](https://mlsanity.com/paper/2107.10050/) (2021)
- [Real-time Human-Centric Segmentation for Complex Video Scenes](https://mlsanity.com/paper/2108.07199/) (2021)
- [Imagine the Unseen: Occluded Pedestrian Detection via Adversarial Feature Completion](https://mlsanity.com/paper/2405.01311/) (2024)
- [Revisiting the Evaluation of Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/2511.10308/) (2025)
- [A PST Algorithm for FPs Suppression in Two-stage CNN Detection Methods](https://mlsanity.com/paper/2406.18553/) (2024)
- [Mean Height Aided Post-Processing for Pedestrian Detection](https://mlsanity.com/paper/2408.13646/) (2024)
- [RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos](https://mlsanity.com/paper/2411.18164/) (2024)

## Abstract (arXiv)

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusion and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression~(NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, a novel ground truth target is designed to alleviate the difficulties caused by the attribute configuration and extremely class imbalance issues during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on two benchmark datasets including CityPersons and CrowdHuman. The experimental results show that our approach outperforms state-of-the-art methods at a large margin on pedestrian detection.