Lightweight siamese object tracking algorithm based on SiamBAN

Cong Tian; Hongyu Chu; Taiqi He; Yanhua Shao; Haode Shi

doi:10.1117/12.3032063

8 June 2024 Lightweight siamese object tracking algorithm based on SiamBAN

Cong Tian, Hongyu Chu, Taiqi He, Yanhua Shao, Haode Shi

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 1317107 (2024) https://doi.org/10.1117/12.3032063
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

The UAV platform has limited computing resources, and the tracking algorithm needs better speed and accuracy tradeoff, a lightweight siamese network target tracking algorithm called SiamBAN-T based on SiamBAN. Firstly, to reduce the number of network parameters, mobilenetV3 was as to extract the siamese feature. Secondly, we introduce CA attention into the feature fusion module to enhance perception ability regarding target spatial-position information. Thirdly, multibranch cross correlation is incorporated into the head of the network to strengthen boundary information and scale information, thereby improving the anti-interference capability of our trackier. Finally, a feature enhancement module is designed to improve classification and regression abilities. Experimental results on UAV123 dataset demonstrate that compared with the original algorithm, our improved algorithm achieves an increase in success rate by 0.8% and accuracy by 0.8%. The running speed has been enhanced by 7.6 times for PC devices and 18.5 times for airborne mobile terminals, respectively. These experimental findings indicate that our SiamBAN-T significantly enhances tracking speed while maintaining high precision.

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

Citation Download Citation

Cong Tian, Hongyu Chu, Taiqi He, Yanhua Shao, and Haode Shi "Lightweight siamese object tracking algorithm based on SiamBAN", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 1317107 (8 June 2024); https://doi.org/10.1117/12.3032063

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KEYWORDS

Feature extraction

Head

Convolution

Feature fusion

Network architectures

Unmanned aerial vehicles

Chlorine

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