Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
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DOI：
10.1007/s11263-012-0550-7
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发表时间：
2013-02-01
影响因子：
19.5
通讯作者：
Sukthankar, Rahul
中科院分区：
文献类型：
--
作者：
Ellis, Chris;Masood, Syed Zain;Sukthankar, Rahul
设计交互式、基于动作的界面的一个重要方面是以最小的延迟可靠地识别动作。高延迟会导致系统的反馈滞后于用户操作，从而显著降低用户体验的交互性。本文提出了识别动作时减少延迟的算法。我们使用延迟感知学习公式来训练基于逻辑回归的分类器，该分类器自动从数据中确定独特的规范姿势，并使用这些姿势来鲁棒地识别存在模糊姿势的动作。为了我们的实验目的，我们引入了一个新的（公开发布的）数据集。我们的方法对词袋和条件随机场（CRF）分类器的比较表明，提高了识别性能的预分割和在线分类任务。此外，我们使用GentleBoost来减少我们的功能集，并进一步改善我们的结果。然后，我们提出的实验，探讨了不同数量的行动的准确性/延迟权衡。最后，我们评估我们的算法在两个现有的数据集。
An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system's feedback to lag behind user actions and thus significantly degrades the interactivity of the user experience. This paper presents algorithms for reducing latency when recognizing actions. We use a latency-aware learning formulation to train a logistic regression-based classifier that automatically determines distinctive canonical poses from data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks. Additionally, we employ GentleBoost to reduce our feature set and further improve our results. We then present experiments that explore the accuracy/latency trade-off over a varying number of actions. Finally, we evaluate our algorithm on two existing datasets.