Fine-Grained Underwater Visual Object Tracking via Long-Term Template Refinement

With the growing demand for underwater recognition systems, underwater visual object tracking has recently received wide attention. The visible light scene analysis in a liquid environment poses specific challenges for underwater visual object tracking, such as impairment of sharpness, blue tinge, and illumination refraction. These problems lead to a performance degradation of the conventional visual object tracking algorithms. In this paper, we propose to adapt the methodology for underwater tracking by performing a long-term template refinement to achieve fine-grained spatial template matching and long-range temporal dependency for use with a Siamese tracking network. The template refinement is based on an attention mechanism, which is designed to highlight the saliency and discriminatory information of the tracked content. This mechanism obtains an accurate target mask for the template and alleviates the impact of the underwater noise in the template feature embedding. The long-term template generator updates the dynamic appearance of the target adaptively by clustering historical templates using a Gaussian Mixture Model. This approach enhances the target representation capacity and alleviates challenges such as visual obstructions, complex background variations and prevalent noise in underwater environment. The experimental results obtained on the challenging underwater tracking dataset, UOT32, demonstrate the advantage of the proposed long-term template refinement tracker. It outperforms state-of-the-art approaches such as SiamMask by 5.03% in Distance Precision and 2.31% in Overlap Success, which provides a promising visual perception technology for underwater artificial intelligence systems. On the standard tracking benchmarking dataset VOT2018, the proposed method is 5.56% higher than SiamRPN++ on the Expected Average Overlap and demonstrates comparable performance, verifying its generalisation capacity in other tracking scenarios and environments.