Video-based analysis of human motion

基于视频的人体运动分析

• 批准号: 105391-2010
• 负责人: Fleet, David
• 金额: $ 3.72万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=545127
• 关键词: Video; based; analysis; human; motion

We spend a lot of time looking at people. Arguably, future computer systems will do so too, enabling new man-machine interfaces, perceptive consumer devices, smart surveillance systems, and myriad systems we have yet to envision. A key enabling technology for such purposes is video-based human pose tracking. The fidelity with which one needs to estimate 3D pose varies from task to task, but for most interesting applications current techniques are insufficient, especially when compared to the subtle movements with which humans communicate. The problems stem from the sheer number of joints in the body, which entails a high-dimensional estimation problem. Soft tissue and clothing obscure the skeleton, producing great variability in appearance, and further ambiguities are due to limited image resolution, the inability to distinguish which parts of an image correspond to which person, and because some body parts are occluded. To resolve ambiguities and reduce uncertainty caused by noisy (or missing) measurements, prior models play a key role in pose tracking, resolving an otherwise ambiguous estimation problem with a bias toward plausible human motions.

我们花了很多时间观察人们。 可以说，未来的计算机系统也将这样做，使新的人机界面，感知消费设备，智能监控系统和无数的系统，我们还没有设想。 实现这一目的的一项关键技术是基于视频的人体姿势跟踪。 人们需要估计3D姿态的保真度因任务而异，但对于大多数有趣的应用程序来说，当前的技术是不够的，特别是与人类交流的微妙运动相比。 这些问题源于身体中关节的绝对数量，这需要高维估计问题。 软组织和衣服模糊了骨架，产生了很大的外观变化，进一步的模糊性是由于有限的图像分辨率，无法区分图像的哪些部分对应于哪个人，以及因为一些身体部位被遮挡。 为了解决模糊性和减少由噪声（或缺失）测量引起的不确定性，先验模型在姿态跟踪中起着关键作用，解决了对合理人类运动有偏见的模糊估计问题。