Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation
Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation
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DOI：
10.1109/tip.2012.2206035
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发表时间：
2012-10-01
影响因子：
10.6
通讯作者：
Di Stefano, Luigi
中科院分区：
文献类型：
--
作者：
Salti, Samuele;Cavallaro, Andrea;Di Stefano, Luigi
长期视频跟踪对于现实场景中的许多应用非常重要。实现长期跟踪的关键组件是跟踪器根据不断变化的条件更新其目标内部表示（外观模型）的能力。鉴于该研究领域快速但分散的发展，我们提出了一个统一的外观模型适应概念框架，可以对不同方法进行原则性比较。此外，我们引入了一种新颖的评估方法，可以同时分析跟踪精度和跟踪成功，而无需设置依赖于应用程序的阈值。基于所提出的框架和这种新颖的评估方法，我们对执行外观模型适应的跟踪器进行了广泛的实验比较。理论和实验分析使我们能够确定最有效的方法，并强调有利于更新过程中错误恢复的设计选择。我们通过实验比较选出了一系列关键的开放研究挑战来结束本文。
Long-term video tracking is of great importance for many applications in real-world scenarios. A key component for achieving long-term tracking is the tracker's capability of updating its internal representation of targets (the appearance model) to changing conditions. Given the rapid but fragmented development of this research area, we propose a unified conceptual framework for appearance model adaptation that enables a principled comparison of different approaches. Moreover, we introduce a novel evaluation methodology that enables simultaneous analysis of tracking accuracy and tracking success, without the need of setting application-dependent thresholds. Based on the proposed framework and this novel evaluation methodology, we conduct an extensive experimental comparison of trackers that perform appearance model adaptation. Theoretical and experimental analyses allow us to identify the most effective approaches as well as to highlight design choices that favor resilience to errors during the update process. We conclude the paper with a list of key open research challenges that have been singled out by means of our experimental comparison.