Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re-Identification
Md Kamal Uddin1, Amran Bhuiyan2, Mahmudul Hasan3
1Md Kamal Uddin*, Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh.
2Amran Bhuiyan, Department. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh.
3Mahmudul Hasan, Department. of Computer Science and Engineering, Comilla University, Comilla, Bangladesh.
Manuscript received on October 16, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on October 30, 2021. | PP: 69-75 | Retrieval Number: 100.1/ijitee.L956610101221 | DOI: 10.35940/ijitee.L9566.10101221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Re-identification, RGB-D features, Skeleton information, Video surveillance.