  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}
