  Tracking transforming objects holds significant importance in various fields
due to the dynamic nature of many real-world scenarios. By enabling systems
accurately represent transforming objects over time, tracking transforming
objects facilitates advancements in areas such as autonomous systems,
human-computer interaction, and security applications. Moreover, understanding
the behavior of transforming objects provides valuable insights into complex
interactions or processes, contributing to the development of intelligent
systems capable of robust and adaptive perception in dynamic environments.
However, current research in the field mainly focuses on tracking generic
objects. In this study, we bridge this gap by collecting a novel dedicated
Dataset for Tracking Transforming Objects, called DTTO, which contains 100
sequences, amounting to approximately 9.3K frames. We provide carefully
hand-annotated bounding boxes for each frame within these sequences, making
DTTO the pioneering benchmark dedicated to tracking transforming objects. We
thoroughly evaluate 20 state-of-the-art trackers on the benchmark, aiming to
comprehend the performance of existing methods and provide a comparison for
future research on DTTO. With the release of DTTO, our goal is to facilitate
further research and applications related to tracking transforming objects.
