A UAV object tracking algorithm based on α-EIOU-NET

To address the issues of existing algorithms related to inaccurate estimation of rectangular boxes and tracking box drift caused by fast-moving objects, leading to the loss of tracking targets, we propose a UAV object tracking algorithm based on the α-EIOU-NET. Initially, we introduce the EIOU-NET, which employs EIOU as a metric for state estimation to optimize parameters. EIOU-NET constraints focus on minimizing deviations in center position and the width and height of predicted rectangular boxes by the tracker. Subsequently, we introduce α to dynamically adjust the weights of loss values and gradients to expedite model convergence, termed α-EIOU. This adaptation significantly enhances both convergence speed and positioning accuracy. Finally, we utilize a spatial-channel attention mechanism and a lightweight feature extraction backbone network, ResNet34, to optimize features, ensuring a balance between algorithm speed and accuracy. Validation experiments were conducted using the UAV123 and Visdrone2019 UAV datasets, along with the GOT10k dataset. Experimental results demonstrate the effectiveness and feasibility of the algorithm, showcasing strong performance in diverse tracking environments, achieving a running speed of 30 fps.