Human body pose detection using Bayesian spatio-temporal templates
Human body pose detection using Bayesian spatio-temporal templates
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DOI：
10.1016/j.cviu.2006.07.007
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发表时间：
2006-11-01
影响因子：
4.5
通讯作者：
Fua, P.
中科院分区：
文献类型：
--
作者：
Dimitrijevic, M.;Lepetit, V.;Fua, P.
我们提出了一种基于模板的方法来检测特定步行姿势的人体轮廓。我们的模板由从动作捕捉数据获得的二维轮廓的短序列组成。这使我们能够将运动信息融入其中，并有助于区分以可预测方式移动的实际人与轮廓大致类似于人类的静态物体。此外，在训练阶段，我们使用统计学习技术来估计和存储不同轮廓部分与识别任务的相关性。在运行时，我们使用它将切角距离转换为有意义的概率估计。这些模板可以处理六种不同的相机视图（不包括正面视图和后视图）以及不同的比例。我们使用室内和室外的人们在杂乱的背景前行走的序列并通过移动摄像机采集来证明我们的技术的有效性，这使得背景减除等技术变得不切实际。 (c) 2006 Elsevier Inc. 保留所有权利。
We present a template-based approach to detecting human silhouettes in a specific walking pose. Our templates consist of short sequences of 2D silhouettes obtained from motion capture data. This lets us incorporate motion information into them and helps distinguish actual people who move in a predictable way from static objects whose outlines roughly resemble those of humans. Moreover, during the training phase we use statistical learning techniques to estimate and store the relevance of the different silhouette parts to the recognition task. At run-time, we use it to convert Chamfer distance to meaningful probability estimates. The templates can handle six different camera views, excluding the frontal and back view, as well as different scales. We demonstrate the effectiveness of our technique using both indoor and outdoor sequences of people walking in front of cluttered backgrounds and acquired with a moving camera, which makes techniques such as background subtraction impractical. (c) 2006 Elsevier Inc. All rights reserved.