Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI
Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI
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计算机视觉 - ECCV 2014 - 第 13 届欧洲会议，瑞士苏黎世，2014 年 9 月 6-12 日，会议记录，第六部分
DOI：
10.1007/978-3-319-10599-4_52
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Pfister T
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文献类型：
--
作者：
Pfister T
本文的目标是识别视频中的手势--定位手势并将其归入多个类别中的一个类别。我们表明，从单个(强监督)训练样本学习的手势分类器的性能可以通过使用弱监督手势样本的“库”来显著提高(并且其性能优于单独从单次示例或库中学习)。一次拍摄的例子和弱监督储存库来自不同的领域(不同的人、不同的视频、连续或非连续的手势等)，我们提出了一种针对人体姿势和手形的领域适应方法，使手势学习方法能够在它们之间泛化。我们还展示了使用最近引入的全局对齐核[12]而不是通常用于时间对齐的标准动态时间扭曲的好处。领域适应和学习方法在两个大规模具有挑战性的手势数据集上进行了评估：一个用于手语，另一个用于意大利语手势。在这两种情况下，性能都超过了之前公布的结果，包括2013年ChaLearn挑战赛中最佳的仅限骨骼分类的条目。
The objective of this paper is to recognize gestures in videos – both localizing the gesture and classifying it into one of multiple classes.We show that the performance of a gesture classifier learnt from a single (strongly supervised) training example can be boosted significantly using a ‘reservoir’ of weakly supervised gesture examples (and that the performance exceeds learning from the one-shot example or reservoir alone). The one-shot example and weakly supervised reservoir are from different ‘domains’ (different people, different videos, continuous or non-continuous gesturing,etc), and we propose a domain adaptation method for human pose and hand shape that enables gesture learning methods to generalise between them. We also show the benefits of using the recently introduced Global Alignment Kernel [12], instead of the standard Dynamic Time Warping that is generally used for time alignment.The domain adaptation and learning methods are evaluated on two large scale challenging gesture datasets: one for sign language, and the other for Italian hand gestures. In both cases performance exceeds the previous published results, including the best skeleton-classification-only entry in the 2013 ChaLearn challenge.