Two-Stream RGB-D Human Detection Algorithm Based on RFB Network
In order to effectively combine RGB image features with depth image features for human detection, this paper proposes a two-stream RGB-D human detection algorithm based on RFB network. The proposed algorithm mainly contains three parts: RGB-stream, Depth-stream and Channel Weight Fusion (CWF) strate...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9134743/ |
| _version_ | 1818624197390237696 |
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| author | Wenli Zhang Jiaqi Wang Xiang Guo Kaizhen Chen Ning Wang |
| author_facet | Wenli Zhang Jiaqi Wang Xiang Guo Kaizhen Chen Ning Wang |
| author_sort | Wenli Zhang |
| collection | DOAJ |
| description | In order to effectively combine RGB image features with depth image features for human detection, this paper proposes a two-stream RGB-D human detection algorithm based on RFB network. The proposed algorithm mainly contains three parts: RGB-stream, Depth-stream and Channel Weight Fusion (CWF) strategy. (1) The RGB-stream extracts RGB image features using RFB-Net as the backbone network. (2) By analyzing the results of depth features visualization, we build the Depth-stream, which can effectively extract the depth image features. (3) The improved CWF strategy can enhance the effectiveness of important channels in RGB-D fusion features and improve the capability of the network expression. The experimental results show that the proposed algorithm has a significant improvement compared with other algorithms on two common datasets. |
| first_indexed | 2024-12-16T18:53:08Z |
| format | Article |
| id | doaj.art-789893c8ecd6467fa80b42f2798adb44 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:53:08Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-789893c8ecd6467fa80b42f2798adb442022-12-21T22:20:36ZengIEEEIEEE Access2169-35362020-01-01812317512318110.1109/ACCESS.2020.30076119134743Two-Stream RGB-D Human Detection Algorithm Based on RFB NetworkWenli Zhang0https://orcid.org/0000-0003-3151-5755Jiaqi Wang1https://orcid.org/0000-0002-1708-3573Xiang Guo2https://orcid.org/0000-0003-3395-1737Kaizhen Chen3https://orcid.org/0000-0001-6871-4091Ning Wang4https://orcid.org/0000-0002-9863-0275Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaIn order to effectively combine RGB image features with depth image features for human detection, this paper proposes a two-stream RGB-D human detection algorithm based on RFB network. The proposed algorithm mainly contains three parts: RGB-stream, Depth-stream and Channel Weight Fusion (CWF) strategy. (1) The RGB-stream extracts RGB image features using RFB-Net as the backbone network. (2) By analyzing the results of depth features visualization, we build the Depth-stream, which can effectively extract the depth image features. (3) The improved CWF strategy can enhance the effectiveness of important channels in RGB-D fusion features and improve the capability of the network expression. The experimental results show that the proposed algorithm has a significant improvement compared with other algorithms on two common datasets.https://ieeexplore.ieee.org/document/9134743/RGB-Dhuman detectionfusion featurestwo-stream |
| spellingShingle | Wenli Zhang Jiaqi Wang Xiang Guo Kaizhen Chen Ning Wang Two-Stream RGB-D Human Detection Algorithm Based on RFB Network IEEE Access RGB-D human detection fusion features two-stream |
| title | Two-Stream RGB-D Human Detection Algorithm Based on RFB Network |
| title_full | Two-Stream RGB-D Human Detection Algorithm Based on RFB Network |
| title_fullStr | Two-Stream RGB-D Human Detection Algorithm Based on RFB Network |
| title_full_unstemmed | Two-Stream RGB-D Human Detection Algorithm Based on RFB Network |
| title_short | Two-Stream RGB-D Human Detection Algorithm Based on RFB Network |
| title_sort | two stream rgb d human detection algorithm based on rfb network |
| topic | RGB-D human detection fusion features two-stream |
| url | https://ieeexplore.ieee.org/document/9134743/ |
| work_keys_str_mv | AT wenlizhang twostreamrgbdhumandetectionalgorithmbasedonrfbnetwork AT jiaqiwang twostreamrgbdhumandetectionalgorithmbasedonrfbnetwork AT xiangguo twostreamrgbdhumandetectionalgorithmbasedonrfbnetwork AT kaizhenchen twostreamrgbdhumandetectionalgorithmbasedonrfbnetwork AT ningwang twostreamrgbdhumandetectionalgorithmbasedonrfbnetwork |