Video-based analysis of human motion

我们花了很多时间观察人们。可以说，未来的计算机系统也会这样做，使新的人机界面、感知消费者设备、智能监控系统和无数我们尚未设想的系统成为可能。实现这一目的的关键技术是基于视频的人体姿势跟踪。估计3D姿态所需的保真度因任务而异，但对于大多数有趣的应用，当前的技术是不够的，特别是与人类交流的微妙动作相比。这些问题源于人体中关节的数量，这需要一个高维估计问题。软组织和衣服模糊了骨骼，在外观上产生很大的变化，进一步的模糊是由于有限的图像分辨率，无法区分图像的哪个部分对应于哪个人，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，先前的模型在姿态跟踪中起着关键作用，解决了对似是而非的人类运动的模糊估计问题。

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.