Tracking Transforming Objects: A Benchmark
You Wu, Yuelong Wang, Yaxin Liao, Fuliang Wu, Hengzhou Ye, Shuiwang Li

TL;DR
This paper introduces DTTO, a new benchmark dataset for tracking transforming objects, evaluates current trackers on it, and aims to advance research in dynamic object tracking.
Contribution
The study presents the first dedicated dataset for tracking transforming objects and provides comprehensive evaluation of existing tracking methods on this benchmark.
Findings
20 state-of-the-art trackers evaluated
DTTO contains 100 sequences with 9.3K frames
Benchmark results highlight current method limitations
Abstract
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…
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Taxonomy
TopicsMusic Technology and Sound Studies · Teleoperation and Haptic Systems · Human Motion and Animation
