Receptive-field adaptive region proposal network for accurate pedestrian detection

Feiteng Han; Ming Xue; Yongqiang Liu; Yudong Fang; Wei Guo; Tao Feng; Wang Chen

doi:10.1117/12.3040216

4 September 2024 Receptive-field adaptive region proposal network for accurate pedestrian detection

Feiteng Han, Ming Xue, Yongqiang Liu, Yudong Fang, Wei Guo, Tao Feng, Wang Chen

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132591Z (2024) https://doi.org/10.1117/12.3040216
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

In the realm of computer vision, pedestrian detection is a pivotal and indispensable task with holding significant relevance for a multitude of applications. The progression in general object detection has propelled two-stage pedestrian detection algorithms to the forefront of performance. However, current works predominantly concentrate on overcoming occlusion challenges, while certain intrinsic constraints within the two-stage framework remain overlooked. This study identifies a feature mismatch issue within the pedestrian region proposal extraction module as a pivotal factor affecting the detection efficacy of two-stage pedestrian detectors. Therefore, we propose a receptive-field adaptive region proposal network and it harnesses the power of multi-receptive field features and deformable convolution, thereby mitigating the feature mismatch. Experimental results suggest that our proposed region proposal network markedly enhances the performance of two-stage pedestrian detection algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Feiteng Han, Ming Xue, Yongqiang Liu, Yudong Fang, Wei Guo, Tao Feng, and Wang Chen "Receptive-field adaptive region proposal network for accurate pedestrian detection", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132591Z (4 September 2024); https://doi.org/10.1117/12.3040216

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KEYWORDS

Object detection

Convolution

Feature extraction

Detection and tracking algorithms

Deformation

Education and training

Sensors

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