Application of improved siamese network UAV tracking algorithm in cattle farm

To improve the real-time performance and robustness of unmanned aerial vehicle target tracking algorithms in cattle farms, with the unmanned aerial vehicle cattle tracking events as the research object, a target tracking algorithm based on siamese tracker with residual accumulation template（SiamRAT） was proposed in the experiment. The lightweight convolutional network MobileNetV2 and template updating mechanism based on anchor box ratio changes was used to improve the real-time performance of algorithms, and the residual accumulation template with confidence and multi-peak Euclidean distance detection module was used to solve the tracking drift issues caused by similar cattle interference. Finally, the SiamRAT algorithm was compared with SiamRPN++, SiamDW, DaSiamRPN, SiamRPN, and ECO-HC algorithms on a test dataset consisting of cattle videos collected by drones and videos with the same attributes in the VOT2018 dataset. The performance was evaluated using average accuracy, robustness, and frame per second（FPS） as indicators, and analyzed the contribution of improvement modules（including residual accumulation template, high confidence update, and peak distance detection） to the SiamRAT algorithm. The results showed that compared with SiamRPN++, SiamDW, DaSiamRPN, SiamRPN, and ECO-HC algorithms, SiamRAT algorithm had the best robustness, and the average accuracy decreased slightly, but it still ranked the second among all algorithms; FPS had significantly improved compared to SiamRPN++ algorithm, with better performance.The robustness and FPS of the improved SiamRAT algorithm had been significantly improved, with an average accuracy of 0.909.It indicated that the SiamRAT algorithm could be well applied to the tracking environment of unmanned aerial vehicles in cattle farms.