  Visual tracking has advanced significantly in recent years, mainly due to the
availability of large-scale training datasets. These datasets have enabled the
development of numerous algorithms that can track objects with high accuracy
and robustness.However, the majority of current research has been directed
towards tracking generic objects, with less emphasis on more specialized and
challenging scenarios. One such challenging scenario involves tracking
reflected objects. Reflections can significantly distort the appearance of
objects, creating ambiguous visual cues that complicate the tracking process.
This issue is particularly pertinent in applications such as autonomous
driving, security, smart homes, and industrial production, where accurately
tracking objects reflected in surfaces like mirrors or glass is crucial. To
address this gap, we introduce TRO, a benchmark specifically for Tracking
Reflected Objects. TRO includes 200 sequences with around 70,000 frames, each
carefully annotated with bounding boxes. This dataset aims to encourage the
development of new, accurate methods for tracking reflected objects, which
present unique challenges not sufficiently covered by existing benchmarks. We
evaluated 20 state-of-the-art trackers and found that they struggle with the
complexities of reflections. To provide a stronger baseline, we propose a new
tracker, HiP-HaTrack, which uses hierarchical features to improve performance,
significantly outperforming existing algorithms. We believe our benchmark,
evaluation, and HiP-HaTrack will inspire further research and applications in
tracking reflected objects. The TRO and code are available at
https://github.com/OpenCodeGithub/HIP-HaTrack.
