TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
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DOI：
10.1109/wacv48630.2021.00101
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Heng Fan;Fan Yang;Peng Chu;Lin Yuan;Haibin Ling
中科院分区：
文献类型：
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作者：
Heng Fan;Fan Yang;Peng Chu;Lin Yuan;Haibin Ling
由于许多挑战因素（例如，遮挡、模糊等）。这些因素中的每一个都可能给跟踪器带来严重的问题，当它们一起工作时，事情会变得更加复杂。尽管大量的努力致力于了解跟踪器的行为，可靠的和可量化的方法来研究每个因素的跟踪行为仍然很少。为了解决这个问题，在本文中，我们贡献给社会的跟踪诊断工具包，TracKlinic，跟踪算法的挑战因素的诊断。TracKlinic包括两个新的组件，分别侧重于数据和分析方面。对于数据部分，我们仔细准备了一组2,390个带注释的视频，每个视频都涉及一个且只有一个主要挑战因素。当分析一个算法的一个特定的挑战因素，这种一个因素一个序列的规则大大抑制了其他因素的干扰，从而导致更忠实的分析。对于分析组件，给定所有序列的跟踪结果，它会调查跟踪器在每个单独因素下的行为并自动生成报告。使用TracKlinic，对10个最先进的跟踪器进行了9个挑战因素（包括两个复合因素）的全面研究。结果表明，严重的形状变化和遮挡是大多数跟踪器面临的两个最具挑战性的因素。此外，视野外，虽然不经常发生，但往往是致命的。通过共享TracKlinic 1，我们希望能够更轻松地诊断跟踪算法，从而促进开发更好的算法。
Generic visual object tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracker, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of trackers, reliable and quantifiable ways for studying the per factor tracking behavior remain barely available. Addressing this issue, in this paper we contribute to the community a tracking diagnosis toolkit, TracKlinic, for diagnosis of challenge factors of tracking algorithms.TracKlinic consists of two novel components focusing on the data and analysis aspects, respectively. For the data component, we carefully prepare a set of 2,390 annotated videos, each involving one and only one major challenge factor. When analyzing an algorithm for a specific challenge factor, such one-factor-per-sequence rule greatly inhibits the disturbance from other factors and consequently leads to more faithful analysis. For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically. With TracKlinic, a thorough study is conducted on ten state-of-the-art trackers on nine challenge factors (including two compound ones). The results suggest that, heavy shape variation and occlusion are the two most challenging factors faced by most trackers. Besides, out-of-view, though does not happen frequently, is often fatal. By sharing TracKlinic 1, we expect to make it much easier for diagnosing tracking algorithms, and to thus facilitate developing better ones.