LAUSR

Object tracking using deep learning is a crucial research direction within intelligent vision processing. One of the key challenges in object tracking is accurately predicting the object’s motion direction in consecutive frames while accounting for the reliability of the tracking results during template updates. In this work, we propose an innovative object-tracking algorithm that leverages both motion direction and time series information. We propose a loss function that guides the tracking model to learn the direction of object motion between consecutive frames, resulting in improved object localization accuracy. Furthermore, to enhance the algorithm’s ability to discriminate the reliability of tracking results and improve the quality of template updates, the proposed approach includes an attention mechanism-based tracking result reliability scoring module, which takes into account the time series of tracking results. Compressive experiment evaluation on four datasets shows our algorithm effectively improves the performances of object tracking. The ablation experiments and qualitative analysis prove the effectiveness of the proposed module and loss function.