Work

Transformer based Object Re-Identification Using Multi-Scale Feature Interaction and Perception

Year: 2025

Type: article

Abstract: Object re-identification aims to match images or videos captured by different cameras. Its objective is to identify the same object when provided with a query image from a collection of images recorded by diverse surveillance devices. Object re-identification tasks still face huge challenges, such as low resolution of images taken in complex scenes, occlusion phenomena in images, changes in lighting, and changes in perspective and posture, especially for 3D reconstruction tasks during power distribution network operations. The recognition accuracy of object re-identification in complex scenarios needs to be further improved. Previous work did not pay attention to the impact of multi-scale feature interaction. This article proposes a novel multi-scale feature interaction and perception with transformer method (MSFIP-TM) for object re-identification. It designs a multi-scale perception module to extract multi-scale features, which have the advantage of expanding the receptive field and facilitating the capture of small differences in different feature maps, thereby improving the robustness of object re-identification methods in complex scenes. Superior experimental performance is achieved on two large-scale Market-1501 and DukeMTMC-ReID datasets.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.12.459

Authors: Mingkai Xu, Cong Li, Chunming Liu, Xin Li

Institutions: State Grid Corporation of China (China), State Grid Shandong Electric Power Company (China)

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: State Grid Shandong Electric Power Company

Award: G7977024787