Detecting Humans in RGB-D Data with CNNs

• URL: http://arxiv.org/abs/2207.08064v1
• Date: Sun, 17 Jul 2022 03:17:09 GMT
• Title: Detecting Humans in RGB-D Data with CNNs
• Authors: Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi
• Abstract summary: We propose a novel fusion approach based on the characteristics of depth images. We also present a new depth-encoding scheme, which not only encodes depth images into three channels but also enhances the information for classification.
• Score: 14.283154024458739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs. To combine the detections produced by the two CNNs, we propose a novel fusion approach based on the characteristics of depth images. We also present a new depth-encoding scheme, which not only encodes depth images into three channels but also enhances the information for classification. We conduct experiments on a publicly available RGB-D people dataset and show that our approach outperforms the baseline models that only use RGB data.

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