Object Tracking Benchmark
Object Tracking Benchmark
复制标题
对象跟踪基准
DOI：
10.1109/tpami.2014.2388226
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发表时间：
2015-09-01
影响因子：
23.6
通讯作者：
Yang, Ming-Hsuan
中科院分区：
文献类型：
--
作者：
Wu, Yi;Lim, Jongwoo;Yang, Ming-Hsuan
目标跟踪一直是计算机视觉领域最重要和最活跃的研究领域之一。近年来，人们提出了大量的跟踪算法，并取得了成功。然而，用于评估的序列集通常是不够的，或者有时对于某些类型的算法有偏差。许多数据集没有共同的地面实况对象位置或范围，这使得报告的定量结果之间的比较变得困难。此外，所评估的跟踪算法的初始条件或参数并不相同，因此文献报道的定量结果无法比较，有时甚至是矛盾的。为了解决这些问题，我们使用各种评估标准对最先进的在线对象跟踪算法进行了广泛的评估，以了解这些方法在同一框架内的执行情况。在这项工作中，我们首先构建一个具有地面实况对象位置和范围的大型数据集以进行跟踪，并引入用于性能分析的序列属性。其次，我们将大多数公开可用的跟踪器集成到一个具有统一输入和输出格式的代码库中，以方便大规模的性能评估。第三，我们广泛评估了 31 种算法在 100 个具有不同初始化设置的序列上的性能。通过分析定量结果，我们确定了稳健跟踪的有效方法，并提供了该领域未来潜在的研究方向。
Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.