Occlusion-aware visual object tracking based on multi-template updating Siamese network

Abstract

Visual object tracking is a crucial area of computer vision research. It aims to accurately track objects in videos with challenges such as occlusion, deformation, and lighting variations. Existing algorithms face difficulties when objects leave the camera or reappear after being occluded, and they also struggle to track objects with significant appearance changes. To address these issues, this study proposed a novel tracking algorithm. It combines tracking and detection, enabling global searching when objects are occluded or disappear and redetecting them when they reappear. A multi-template updating mechanism was used to adapt to changes in appearance. The study proposed the OTB100-AB and UAV123-AB datasets to evaluate the tracker's ability to handle target disappearance, along with the TNR metric. The proposed algorithm was evaluated on these datasets, as were OTB50 and UAV20L, which outperformed state-of-the-art algorithms and significantly improved tracking performance.