A lightweight single object tracking algorithm for UAVs based on Siamese network

Currently, some single object tracking algorithms have achieved leading performance, but their large models limit their application on resource-constrained platforms such as Unmanned Aerial Vehicles (UAVs). This paper designs a lightweight single object tracking algorithm for UAVs based on the Siamese network to achieve efficient tracking with less resource consumption. Firstly, a lightweight backbone network for Siamese feature extraction is designed based on MobileNetV3, significantly reducing the computationand parameter volume of the network without compromising feature extraction capabilities. Secondly, a dual cross-correlation module is designed. The module uses Pointwise Cross-Correlation to quickly calculate the similarity between template image featuresand search image featuresand also uses Depthwise Cross-Correlation to supplement missing features, effectively enhancing feature matching accuracyand robustness. Then, a lightweight prediction head is designed by stacking multiple depthwise separable convolution layers, obtaining accurate target representations with minimal performance consumption. Finally, classification ranking loss is introduced to traditional classificationand regression losses, enhancing the network's ability to learn target foregroundand suppress background interference, and further improving tracking performance. Comprehensive experiments show that the proposed algorithm achieves 82.1%, 81.2%, and 64.6% precisionand 63.4%, 61.8%, and 49.6% success rate on DTB70, UAV123, and UAV20L datasets, respectively, with only 5.3×10⁵ parametersand 1.1×10⁸ floating point operations. It achieves the performance comparable to state-of-the-art tracking algorithms, while having significantly fewer parametersand computational load, and it can run at the speeds above 100 fps, meeting the real-time requirements of UAV object tracking.