Person Re-Identification Based on First-Order and Second-Order Spatial Information

In recent years, crossmodal pedestrian reidentification has gradually become one of the hotspots in the field of computer vision. However, it is crucial to effectively extract pedestrian features, further realize the interactive fusion of photos, and mine any potential relationships between pedestrian images while performing crossmodal pedestrian reidentification. To address this issue, a dual stream network based on channel grouping reorganization and attention mechanisms is proposed to extract more stable and rich features between the two modes. Specifically, to extract the shared characteristics of crossmodal images and to achieve the interactive fusion of modal information, the intramodal feature channel grouping rearrangement module (ICGR) was inserted in the backbone network. Furthermore, to extract additional distinct local features, the possible association between pedestrian images captured using various modes was mined using the aggregated feature attention mechanism and crossmodal adaptive graph structure. A large number of experimental results on mainstream datasets such as SYSU -MM01 and RegDB demonstrate that the proposed algorithm has good generalization ability on multiple datasets. The crossmodal pedestrian reidentification algorithm achieves higher accuracy compared with the existing main algorithms. Key words image processing; crossmodal; person reidentification; channel grouping reorganization; attention mechanism

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Person Re-Identification Based on First-Order and Second-Order Spatial Information

Cited by 2 publications

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