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Out of sight : a toolkit for tracking occluded human joint positions

机译:视线外:跟踪被遮挡的人体关节位置的工具包

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摘要

Real-time identification and tracking of the joint positions of people can be achieved with off-the-shelf sensing technologies such as the Microsoft Kinect, or other camera based systems with computer vision. However, tracking is constrained by the system’s field of view of people. When a person is occluded from the camera view, their position can no longer be followed. Out of Sight addresses the occlusion problem in depth-sensing tracking systems. Our new tracking infrastructure provides human skeleton joint positions during occlusion, by combining the field of view of multiple Kinects using geometric calibration and affine transformation.We verified the technique’s accuracy through a system evaluation consisting of 20 participants in stationary position and in motion, with two Kinects positioned parallel, 45,and 90 degrees apart. Results show that our skeleton matching is accurate to within 16.1 cm (s.d. = 5.8 cm), which is within a person’s personal space. In a realistic scenario study, groups of two people quickly occlude each other, and occlusion is resolved for 85% of the participants. A RESTfulAPI was developed to allow distributed access of occlusion free skeleton joint positions. As a further contribution, we provide the system as open source.
机译:可以使用现成的传感技术(例如Microsoft Kinect或其他具有计算机视觉的基于摄像头的系统)来实现对人的关节位置的实时识别和跟踪。但是,跟踪受到系统人员视野的限制。当某人从相机视图中被遮挡时,将无法再跟随其位置。视力问题解决了深度感应跟踪系统中的遮挡问题。我们的新跟踪基础设施通过使用几何校准和仿射变换结合多个Kinect的视野,提供了咬合期间人体骨骼的关节位置。我们通过系统评估验证了该技术的准确性,该系统评估包括20位固定位置和运动的参与者,其中两名Kinect平行放置,相距45度和90度。结果表明,我们的骨骼匹配精确到了16.1厘米(标准误差= 5.8厘米)以内,这在一个人的个人空间内。在一个现实的场景研究中,两个人的小组迅速相互遮挡,解决了85%的参与者遮挡的问题。开发了RESTfulAPI以允许无遮挡的骨骼关节位置的分布式访问。作为进一步的贡献,我们提供了开源的系统。

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