Human Limb Delineation and Joint Position Recovery Using Localized Boundary Models

C. McIntosh, G. Hamarneh, Greg Mori
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引用次数: 19

Abstract

We outline the development of a self-initializing kinematic tracker that automatically discovers its part appearance models from a video sequence. Through its unique combination of an existing global joint estimation technique and a robust physical deformation based local search method, the tracker is demonstrated as a novel approach to recovering 2D human joint locations and limb outlines from video sequences. Appearance models are discovered and employed through a novel use of the deformable organisms framework which we have extended to the temporal domain. Quantitative and qualitative results for a set of five test videos are provided. The results demonstrate an overall improvement in tracking performance and that the method is relatively insensitive to initialization, an important consideration in gradient descent-style search algorithms.
基于局部边界模型的人体肢体描绘和关节位置恢复
我们概述了一种自初始化的运动学跟踪器的发展,该跟踪器可以从视频序列中自动发现其部件外观模型。通过将现有的全局关节估计技术和基于鲁棒物理变形的局部搜索方法相结合,该跟踪器被证明是一种从视频序列中恢复2D人体关节位置和肢体轮廓的新方法。外观模型是通过一种新的使用可变形生物框架发现和采用的,我们已经扩展到时间域。提供了一组五个测试视频的定量和定性结果。结果表明,该方法总体上提高了跟踪性能,并且对初始化不敏感,这是梯度下降式搜索算法的一个重要考虑因素。
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