Performance Metrics for Activity Recognition
Performance Metrics for Activity Recognition
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DOI：
10.1145/1889681.1889687
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发表时间：
2011-01-01
影响因子：
5
通讯作者：
Gellersen, Hans W.
中科院分区：
文献类型：
--
作者：
Ward, Jamie A.;Lukowicz, Paul;Gellersen, Hans W.
在这篇文章中，我们介绍和评估了一套全面的性能指标和可视化的连续活动识别（AR）。我们演示了如何标准的评估方法，往往是从相关的模式识别问题，无法捕捉常见的文物中发现连续AR具体事件碎片，事件合并和定时偏移。我们支持我们的主张与一组最近发表的AR论文的分析。建立在较早的初步工作的主题，我们开发了一个基于框架的可视化和相应的一组类倾斜不变的指标与所有的评价。这些都是由一个新的完整的基于事件的指标集，允许系统性能的快速图形表示，显示正确的事件，插入，删除，碎片，合并和那些既碎片和合并的补充。我们评估我们的方法的实用性，通过比较标准的指标数据从三个不同的实验。这表明，在某些情况下，基于事件和帧的精确度和召回率导致对结果的模糊解释，所提出的指标提供了一致的明确解释。
In this article, we introduce and evaluate a comprehensive set of performance metrics and visualisations for continuous activity recognition (AR). We demonstrate how standard evaluation methods, often borrowed from related pattern recognition problems, fail to capture common artefacts found in continuous AR specifically event fragmentation, event merging and timing offsets. We support our assertion with an analysis on a set of recently published AR papers. Building on an earlier initial work on the topic, we develop a frame-based visualisation and corresponding set of class-skew invariant metrics for the one class versus all evaluation. These are complemented by a new complete set of event-based metrics that allow a quick graphical representation of system performance showing events that are correct, inserted, deleted, fragmented, merged and those which are both fragmented and merged. We evaluate the utility of our approach through comparison with standard metrics on data from three different published experiments. This shows that where event- and frame-based precision and recall lead to an ambiguous interpretation of results in some cases, the proposed metrics provide a consistently unambiguous explanation.