# A PST Algorithm for FPs Suppression in Two-stage CNN Detection Methods

arXiv: https://arxiv.org/abs/2406.18553 · HTML: https://mlsanity.com/paper/2406.18553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 20 of 20)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection](https://mlsanity.com/paper/1803.05347/) (2018)
- [Adaptive NMS: Refining Pedestrian Detection in a Crowd](https://mlsanity.com/paper/1904.03629/) (2019)
- [Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/1802.09972/) (2018)
- [Pelee: A Real-Time Object Detection System on Mobile Devices](https://mlsanity.com/paper/1804.06882/) (2018)
- [Is Faster R-CNN Doing Well for Pedestrian Detection?](https://mlsanity.com/paper/1607.07032/) (2016)
- [NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing](https://mlsanity.com/paper/2003.12729/) (2020)
- [Scale-aware Fast R-CNN for Pedestrian Detection](https://mlsanity.com/paper/1510.08160/) (2015)
- [From Handcrafted to Deep Features for Pedestrian Detection: A Survey](https://mlsanity.com/paper/2010.00456/) (2020)
- [Illuminating Pedestrians via Simultaneous Detection & Segmentation](https://mlsanity.com/paper/1706.08564/) (2017)
- [Attribute-aware Pedestrian Detection in a Crowd](https://mlsanity.com/paper/1910.09188/) (2019)
- [Ten Years of Pedestrian Detection, What Have We Learned?](https://mlsanity.com/paper/1411.4304/) (2014)
- [Learning Multilayer Channel Features for Pedestrian Detection](https://mlsanity.com/paper/1603.00124/) (2016)

## Abstract (arXiv)

Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications, the major challenge of which is False Positives (FPs) that occur during pedestrian detection. The emergence various Convolutional Neural Network-based detection strategies substantially enhance the pedestrian detection accuracy but still not well solve this problem. This paper deeply analysis the detection framework of the two-stage CNN detection methods and find out false positives in detection results is due to its training strategy miss classify some false proposals, thus weakens the classification capability of following subnetwork and hardly to suppress false ones. To solve this problem, This paper proposes a pedestrian-sensitive training algorithm to effectively help two-stage CNN detection methods learn to distinguish the pedestrian and non-pedestrian samples and suppress the false positives in final detection results. The core of the proposed training algorithm is to redesign the training proposal generating pipeline of the two-stage CNN detection methods, which can avoid a certain number of false ones that mislead its training process. With the help of the proposed algorithm, the detection accuracy of the MetroNext, an smaller and accurate metro passenger detector, is further improved, which further decreases false ones in its metro passengers detection results. Based on various challenging benchmark datasets, experiment results have demonstrated that feasibility of the proposed algorithm to improve pedestrian detection accuracy by removing the false positives. Compared with the competitors, MetroNext-PST demonstrates better overall prediction performance in accuracy, total number of parameters, and inference time, thus it can become a practical solution for hunting pedestrian tailored for mobile and edge devices.