Published October 30, 2021 | Version v1
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Fusion in Dissimilarity Space Between RGB-D and Skeleton for Person Re-Identification

  • 1. Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
  • 2. Department. of Computer Science and Engineering, Comilla University, Comilla, Bangladesh
  • 1. Publisher


Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.



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Journal article: 2278-3075 (ISSN)


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