Fusion in Dissimilarity Space Between RGB D and Skeleton for Person Re Identification
| Autor: | Mahmudul Hasan, Amran Bhuiyan, Kamal Uddin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Fusion
General Computer Science Dissimilarity space business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Skeleton (category theory) Re identification Mechanics of Materials Artificial intelligence Electrical and Electronic Engineering business ComputingMethodologies_COMPUTERGRAPHICS Civil and Structural Engineering |
| Zdroj: | International Journal of Innovative Technology and Exploring Engineering. 10:69-75 |
| ISSN: | 2278-3075 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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