Tracking Small and Fast Moving Objects: A Benchmark
Zhewen Zhang, Fuliang Wu, Yuming Qiu, Jingdong Liang, Shuiwang Li

TL;DR
This paper introduces TSFMO, a new benchmark dataset for tracking small and fast-moving objects, especially in sports, and evaluates existing methods while proposing a superior new tracker, S-KeepTrack.
Contribution
The paper presents the first dedicated benchmark for small and fast-moving object tracking and introduces a novel tracker that outperforms existing methods.
Findings
Existing trackers struggle with small and fast-moving objects.
The proposed S-KeepTrack outperforms all evaluated methods.
TSFMO facilitates future research in this challenging area.
Abstract
With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for \textbf{T}racking \textbf{S}mall and \textbf{F}ast \textbf{M}oving \textbf{O}bjects. This benchmark aims to encourage research in developing novel and accurate methods for this challenging task particularly. TSFMO consists of 250 sequences with about 50k frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box. To the best of our knowledge, TSFMO is the first benchmark dedicated to tracking small and fast moving objects, especially connected to sports. To understand how existing methods perform and to provide comparison for future research on TSFMO, we extensively evaluate 20 state-of-the-art…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Innovative Human-Technology Interaction · Video Analysis and Summarization
