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Two-Stream RGB-D Human Detection Algorithm Based on RFB Network
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DOI：10.1109/ACCESS.2020.3007611.png)
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摘要
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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.
Keyword:
RGB-D
human detection
fusion features
two-stream
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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