Optimized Information Flow for Transformer Tracking

One-stream Transformer trackers have shown outstanding performance in challenging benchmark datasets over the last three years, as they enable interaction between the target template and search region tokens to extract target-oriented features with mutual guidance. Previous approaches allow free bidirectional information flow between template and search tokens without investigating their influence on the tracker's discriminative capability. In this study, we conducted a detailed study on the information flow of the tokens and based on the findings, we propose a novel Optimized Information Flow Tracking (OIFTrack) framework to enhance the discriminative capability of the tracker. The proposed OIFTrack blocks the interaction from all search tokens to target template tokens in early encoder layers, as the large number of non-target tokens in the search region diminishes the importance of target-specific features. In the deeper encoder layers of the proposed tracker, search tokens are partitioned into target search tokens and non-target search tokens, allowing bidirectional flow from target search tokens to template tokens to capture the appearance changes of the target. In addition, since the proposed tracker incorporates dynamic background cues, distractor objects are successfully avoided by capturing the surrounding information of the target. The OIFTrack demonstrated outstanding performance in challenging benchmarks, particularly excelling in the one-shot tracking benchmark GOT-10k, achieving an average overlap of 74.6\%. The code, models, and results of this work are available at \url{https://github.com/JananiKugaa/OIFTrack}

Transformers in Single Object Tracking: An Experimental Survey

Single object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single object tracking due to their superior tracking robustness. Although several survey studies have been conducted to analyze the performance of trackers, there is a need for another survey study after the introduction of Transformers in single object tracking. In this survey, we aim to analyze the literature and performances of Transformer tracking approaches. Therefore, we conduct an in-depth literature analysis of Transformer tracking approaches and evaluate their tracking robustness and computational efficiency on challenging benchmark datasets. In addition, we have measured their performances on different tracking scenarios to find their strength and weaknesses. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they face, and their future directions.