# From Handcrafted to Deep Features for Pedestrian Detection: A Survey

arXiv: https://arxiv.org/abs/2010.00456 · HTML: https://mlsanity.com/paper/2010.00456/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

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

- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [UniHCP: A Unified Model for Human-Centric Perceptions](https://mlsanity.com/paper/2303.02936/) (2023)
- [PSTR: End-to-End One-Step Person Search With Transformers](https://mlsanity.com/paper/2204.03340/) (2022)
- [BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection](https://mlsanity.com/paper/2112.02277/) (2021)
- [Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection](https://mlsanity.com/paper/2403.01300/) (2024)
- [Large-Scale Person Detection and Localization using Overhead Fisheye Cameras](https://mlsanity.com/paper/2307.08252/) (2023)
- [Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application](https://mlsanity.com/paper/2301.04829/) (2023)
- [Integrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection](https://mlsanity.com/paper/2311.01025/) (2023)
- [Oriented object detection in optical remote sensing images using deep learning: a survey](https://mlsanity.com/paper/2302.10473/) (2023)
- [ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection](https://mlsanity.com/paper/2111.14055/) (2021)
- [Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2308.02935/) (2023)
- [E-Scooter Rider Detection and Classification in Dense Urban Environments](https://mlsanity.com/paper/2205.10184/) (2022)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search](https://mlsanity.com/paper/2302.04607/) (2023)
- [Local and Global Contextual Features Fusion for Pedestrian Intention Prediction](https://mlsanity.com/paper/2305.01111/) (2023)
- [The Impact of Partial Occlusion on Pedestrian Detectability](https://mlsanity.com/paper/2205.04812/) (2022)
- [An Objective Method for Pedestrian Occlusion Level Classification](https://mlsanity.com/paper/2205.05412/) (2022)
- [Bright Channel Prior Attention for Multispectral Pedestrian Detection](https://mlsanity.com/paper/2305.12845/) (2023)
- [DDAM-PS: Diligent Domain Adaptive Mixer for Person Search](https://mlsanity.com/paper/2310.20706/) (2023)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [Beyond Overall Accuracy: Pose- and Occlusion-driven Fairness Analysis in Pedestrian Detection for Autonomous Driving](https://mlsanity.com/paper/2509.26166/) (2025)
- [Leveraging Prior Knowledge of Diffusion Model for Person Search](https://mlsanity.com/paper/2510.01841/) (2025)
- [WCCNet: Wavelet-context Cooperative Network for Efficient Multispectral Pedestrian Detection](https://mlsanity.com/paper/2308.01042/) (2023)
- [Ranking-based Adaptive Query Generation for DETRs in Crowded Pedestrian Detection](https://mlsanity.com/paper/2310.15725/) (2023)
- [Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges](https://mlsanity.com/paper/2311.08557/) (2023)
- [A PST Algorithm for FPs Suppression in Two-stage CNN Detection Methods](https://mlsanity.com/paper/2406.18553/) (2024)
- [An Earth Rover dataset recorded at the ICRA@40 party](https://mlsanity.com/paper/2407.05735/) (2024)
- [Mean Height Aided Post-Processing for Pedestrian Detection](https://mlsanity.com/paper/2408.13646/) (2024)
- [TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks](https://mlsanity.com/paper/2506.10722/) (2025)
- [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 is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. Here we present a comprehensive survey on recent advances in pedestrian detection. First, we provide a detailed review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches. For handcrafted features based methods, we present an extensive review of approaches and find that handcrafted features with large freedom degrees in shape and space have better performance. In the case of deep features based approaches, we split them into pure CNN based methods and those employing both handcrafted and CNN based features. We give the statistical analysis and tendency of these methods, where feature enhanced, part-aware, and post-processing methods have attracted main attention. In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance. Furthermore, we introduce some related datasets and evaluation metrics, and compare some representative methods. We conclude this survey by emphasizing open problems that need to be addressed and highlighting various future directions. Researchers can track an up-to-date list at https://github.com/JialeCao001/PedSurvey.