RGB-T tracking of efficient feature maps via dual-stream Siamese network

Jinlong Li; Rui Li

doi:10.1117/12.3045422

9 October 2024 RGB-T tracking of efficient feature maps via dual-stream Siamese network

Jinlong Li, Rui Li

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 1328816 (2024) https://doi.org/10.1117/12.3045422
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

Target tracking relying solely on visible images can be unreliable under adverse lighting conditions. In contrast, infrared images, capturing thermal radiation, remain unaffected by such factors. This complementarity has spurred the development of numerous RGB-T tracking methods. However, existing approaches often neglect effective feature extraction across various levels and overlook the computational demands of self-attention mechanisms. Addressing these challenges, we propose SiamEFM, a twin network-based RGB-T tracking algorithm. Our method leverages the Recurrent Cross-Circular Attention (RCCA) mechanism to enhance pixel-level representations, integrates modal information through a feature fusion network, and employs Layer Attention to consolidate features across different levels. Experimental validation on the GTOT dataset demonstrates the competitive performance of our tracker.

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

Citation Download Citation

Jinlong Li and Rui Li "RGB-T tracking of efficient feature maps via dual-stream Siamese network", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 1328816 (9 October 2024); https://doi.org/10.1117/12.3045422

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KEYWORDS

Infrared radiation

Feature fusion

Infrared imaging

Visible radiation

Computer vision technology

Feature extraction

RGB color model

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