Transparent Object Tracking Benchmark
Heng Fan, Halady Akhilesha Miththanthaya, Harshit, Siranjiv Ramana, Rajan, Xiaoqiong Liu, Zhilin Zou, Yuewei Lin, Haibin Ling

TL;DR
This paper introduces the first dedicated benchmark for transparent object tracking, evaluates existing algorithms, reveals insights about their performance, and proposes a new, superior tracker called TransATOM.
Contribution
The paper presents the first transparent object tracking benchmark (TOTB), provides comprehensive evaluation of state-of-the-art trackers, and introduces a novel tracker, TransATOM, that outperforms existing methods.
Findings
Existing trackers perform poorly on transparent objects.
Deeper features are not always beneficial for tracking.
TransATOM significantly outperforms other evaluated algorithms.
Abstract
Visual tracking has achieved considerable progress in recent years. However, current research in the field mainly focuses on tracking of opaque objects, while little attention is paid to transparent object tracking. In this paper, we make the first attempt in exploring this problem by proposing a Transparent Object Tracking Benchmark (TOTB). Specifically, TOTB consists of 225 videos (86K frames) from 15 diverse transparent object categories. Each sequence is manually labeled with axis-aligned bounding boxes. To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking. In order to understand how existing trackers perform and to provide comparison for future research on TOTB, we extensively evaluate 25 state-of-the-art tracking algorithms. The evaluation results exhibit that more efforts are needed to improve transparent object tracking. Besides, we…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Visual Attention and Saliency Detection · Face recognition and analysis
