Towards real-time 3-D monocular visual tracking of human limbs in unconstrained environments

Dave Bullock , John Zelek
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引用次数: 22

Abstract

The 3-D visual tracking of human limbs is fundamental to a wide array of computer vision applications including gesture recognition, interactive entertainment, biomechanical analysis, vehicle driver monitoring, and electronic surveillance. The problem of limb tracking is complicated by issues of occlusion, depth ambiguities, rotational ambiguities, and high levels of noise caused by loose fitting clothing. We attempt to solve the 3-D limb tracking problem using only monocular imagery (a single 2-D video source) in largely unconstrained environments. The approach presented is a movement towards full real-time operating capabilities. The described system presents a complete visual tracking system which incorporates target detection, target model acquisition/initialization, and target tracking components into a single, cohesive, probabilistic framework. The presence of a target is detected, using visual cues alone, by recognition of an individual performing a simple pre-defined initialization cue. The physical dimensions of the limb are then learned probabilistically until a statistically stable model estimate has been found. The appearance of the limb is learned in a joint spatial-chromatic domain which incorporates normalized color data with spatial constraints in order to model complex target appearances. The target tracking is performed within a Monte Carlo particle filtering framework which is capable of maintaining multiple state-space hypotheses and propagating ambiguity until less ambiguous data is observed. Multiple image cues are combined within this framework in a principled Bayesian manner. The target detection and model acquisition components are able to perform at near real-time frame rates and are shown to accurately recognize the presence of a target and initialize a target model specific to that user. The target tracking component has demonstrated exceptional resilience to occlusion and temporary target disappearance and contains a natural mechanism for the trade-off between accuracy and speed. At this point, the target tracking component performs at sub real-time frame rates, although several methods to increase the effective operating speed are proposed.

无约束环境下人体肢体实时三维单目跟踪研究
人体肢体的三维视觉跟踪是广泛的计算机视觉应用的基础,包括手势识别、互动娱乐、生物力学分析、车辆驾驶员监控和电子监控。肢体跟踪的问题由于遮挡、深度模糊、旋转模糊以及宽松衣服引起的高水平噪声等问题而变得复杂。我们试图在很大程度上不受约束的环境中,仅使用单眼图像(单个二维视频源)来解决三维肢体跟踪问题。所提出的方法是向完全实时操作能力的转变。所描述的系统提出了一个完整的视觉跟踪系统,它将目标检测、目标模型获取/初始化和目标跟踪组件合并到一个单一的、内聚的、概率框架中。目标的存在是通过单独使用视觉线索,通过执行简单的预定义初始化线索的个体识别来检测的。肢体的物理尺寸,然后学习概率,直到一个统计上稳定的模型估计被发现。肢体的外观是在一个联合的空间-色域中学习的,该域将归一化的颜色数据与空间约束相结合,以模拟复杂的目标外观。目标跟踪在蒙特卡罗粒子滤波框架内进行,该框架能够维持多个状态空间假设并传播模糊性,直到观察到更少的模糊数据。多个图像线索以贝叶斯原则的方式组合在这个框架内。目标检测和模型获取组件能够以接近实时的帧速率执行,并且能够准确地识别目标的存在并初始化特定于该用户的目标模型。目标跟踪组件显示了对遮挡和暂时目标消失的特殊恢复能力,并包含了在精度和速度之间权衡的自然机制。在这一点上,目标跟踪组件执行亚实时帧速率,尽管提出了几种方法来提高有效运行速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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