期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 2, 期 1, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1889681.1889687
关键词
Performance; Standardization; Activity recognition; metrics; performance evaluation
资金
- EPSRC [EP/H005064/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H005064/1] Funding Source: researchfish
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据