# Is Faster R-CNN Doing Well for Pedestrian Detection?

arXiv: https://arxiv.org/abs/1607.07032 · HTML: https://mlsanity.com/paper/1607.07032/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2016.

## Built on by (most cited first, 30 of 37)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving](https://mlsanity.com/paper/2105.12713/) (2021)
- [Towards Domain Generalization in Object Detection](https://mlsanity.com/paper/2203.14387/) (2022)
- [MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization](https://mlsanity.com/paper/2302.00290/) (2023)
- [Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement](https://mlsanity.com/paper/2103.10621/) (2021)
- [Variational Pedestrian Detection](https://mlsanity.com/paper/2104.12389/) (2021)
- [Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond](https://mlsanity.com/paper/2201.03176/) (2022)
- [NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection](https://mlsanity.com/paper/2106.02426/) (2021)
- [Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU](https://mlsanity.com/paper/2107.11196/) (2021)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Whole-body Detection, Recognition and Identification at Altitude and Range](https://mlsanity.com/paper/2311.05725/) (2023)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [Feature Calibration Network for Occluded Pedestrian Detection](https://mlsanity.com/paper/2212.05717/) (2022)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Explainable, automated urban interventions to improve pedestrian and vehicle safety](https://mlsanity.com/paper/2110.11672/) (2021)
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- [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022)
- [Pedestrian Detection by Exemplar-Guided Contrastive Learning](https://mlsanity.com/paper/2111.08974/) (2021)
- [Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning](https://mlsanity.com/paper/2408.03030/) (2024)
- [Lightweight Jet Reconstruction and Identification as an Object Detection Task](https://mlsanity.com/paper/2202.04499/) (2022)
- [Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning](https://mlsanity.com/paper/2208.08132/) (2022)
- [Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images](https://mlsanity.com/paper/2311.13719/) (2023)
- [Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection](https://mlsanity.com/paper/2103.10091/) (2021)
- [Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection](https://mlsanity.com/paper/2104.13764/) (2021)
- [Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach](https://mlsanity.com/paper/2105.08253/) (2021)
- [Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset](https://mlsanity.com/paper/2112.02221/) (2021)
- [N-RPN: Hard Example Learning for Region Proposal Networks](https://mlsanity.com/paper/2208.01916/) (2022)
- [CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection](https://mlsanity.com/paper/2212.05691/) (2022)
- [Rt-Track: Robust Tricks for Multi-Pedestrian Tracking](https://mlsanity.com/paper/2303.09668/) (2023)
- [Continual Learning for Out-of-Distribution Pedestrian Detection](https://mlsanity.com/paper/2306.15117/) (2023)

## Abstract (arXiv)

Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN [1, 2] have shown excellent performance for general object detection, they have limited success for detecting pedestrian, and previous leading pedestrian detectors were in general hybrid methods combining hand-crafted and deep convolutional features. In this paper, we investigate issues involving Faster R-CNN [2] for pedestrian detection. We discover that the Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a stand-alone pedestrian detector, but surprisingly, the downstream classifier degrades the results. We argue that two reasons account for the unsatisfactory accuracy: (i) insufficient resolution of feature maps for handling small instances, and (ii) lack of any bootstrapping strategy for mining hard negative examples. Driven by these observations, we propose a very simple but effective baseline for pedestrian detection, using an RPN followed by boosted forests on shared, high-resolution convolutional feature maps. We comprehensively evaluate this method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting competitive accuracy and good speed. Code will be made publicly available.