Two-wheeler rider re-identification that combines attention mechanism and local feature fusion

Siheng Sun; Wei Jia

doi:10.1117/12.3035376

19 July 2024 Two-wheeler rider re-identification that combines attention mechanism and local feature fusion

Siheng Sun, Wei Jia

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131L (2024) https://doi.org/10.1117/12.3035376
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Two-wheeler riders primarily refer to drivers of bicycles, electric bicycles, electric motorcycles, and motorcycles. Due to the minimal protection afforded to riders on the road, they are highly vulnerable to injuries and fatalities in traffic accidents. Therefore, the identification and supervision of riders on the road are of significant research importance. To address this issue, this paper proposes a novel rider re-identification algorithm based on deep learning, named the two-wheeler rider re-identification algorithm that combines attention mechanism and local feature fusion (AMLFF-Net). Firstly, we divide the network into global and local branches, where the global branch captures coarse-grained features of the entire image, while the local branch captures fine-grained features of specific regions. Secondly, we introduce attention modules and local feature fusion modules. The attention modules enable the network to focus on the target itself, reducing interference from background factors, while the local feature fusion module merges rider features with two-wheeler features, thus increasing the differentiation between feature vectors of different identities. Finally, we compare the proposed method with existing algorithms on two rider re-identification datasets. Experimental results demonstrate that AMLFF-Net achieves Rank-1 accuracies of 72.6% and 89.8% on the BPReid and MoRe datasets respectively, with mAP scores of 73.7% and 91.4% respectively, outperforming other methods and confirming the effectiveness of our proposed approach.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siheng Sun and Wei Jia "Two-wheeler rider re-identification that combines attention mechanism and local feature fusion", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131L (19 July 2024); https://doi.org/10.1117/12.3035376

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KEYWORDS

Feature fusion

Image fusion

Machine learning

Roads

Feature extraction

Detection and tracking algorithms

Intelligence systems

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