Detecting Humans in RGB-D Data with CNNs

Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


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.
Original languageEnglish
Title of host publicationIAPR Conference on Machine Vision Applications (MVA2017)
Publication statusAccepted/In press - 13 Feb 2017


  • Digital Health


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