3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion

Fang Tan, Zhaoqiang Xia, Yupeng Ma, Xiaoyi Feng

科研成果: 期刊稿件文章同行评审

17 引用 (Scopus)

摘要

Pedestrian detection is vitally important in many computer vision tasks but still suffers from some problems, such as illumination and occlusion if only the RGB image is exploited, especially in outdoor and long-range scenes. Combining RGB with depth information acquired by 3D sensors may effectively alleviate these problems. Therefore, how to utilize depth information and how to fuse RGB and depth features are the focus of the task of RGB-D pedestrian detection. This paper first improves the most commonly used HHA method for depth encoding by optimizing the gravity direction extraction and depth values mapping, which can generate a pseudo-color image from the depth information. Then, a two-branch feature fusion extraction module (TFFEM) is proposed to obtain the local and global features of both modalities. Based on TFFEM, an RGB-D pedestrian detection network is designed to locate the people. In experiments, the improved HHA encoding method is twice as fast and achieves more accurate gravity-direction extraction on four publicly-available datasets. The pedestrian detection performance of the proposed network is validated on KITTI and EPFL datasets and achieves state-of-the-art performance. Moreover, the proposed method achieved third ranking among all published works on the KITTI leaderboard. In general, the proposed method effectively fuses RGB and depth features and overcomes the effects of illumination and occlusion problems in pedestrian detection.

源语言英语
文章编号645
期刊Remote Sensing
14
3
DOI
出版状态已出版 - 1 2月 2022

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