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首页> 外文会议>2013 Seventh International Conference on Distributed Smart Cameras >Multi-view hand tracking using epipolar geometry-based consistent labeling for an industrial application
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Multi-view hand tracking using epipolar geometry-based consistent labeling for an industrial application

机译：使用基于对极几何的一致标记的多视图手跟踪，用于工业应用

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This paper addresses a visual tracking and analysis method for automatic monitoring of an industrial manual assembly process, where each worker sequentially picks up components from different boxes during an assembling process. Automatic surveillance of assembling process would enable to reduce assembling errors by giving early warning. We propose a hand tracking and trajectory analysis method from videos captured by several uncalibrated cameras with overlapping views. The proposed method consists of three modules through single-view hand tracking, consistent labeling across views, and optimal decision from multi-view temporal dynamics. The main novelties of the paper include: (a) target model learning with multiple instances through K-means clustering applied to accommodate different levels of light reflection; (b) optimal criterion for consistent labeling of tracked hands across views, based on the symmetric epipolar distance; (c) backward correction of mis-detection by combining epipolar lines with previously tracked results; (d) a multi-view voting scheme for analyzing hand trajectory using binary hand location maps. Experiments have been conducted on videos by multiple uncalibrated cameras, where a person performs assembly operations. Test results and performance evaluation have shown the effectiveness of this method, in terms of multi-view consistent estimation of hand trajectories and accurate interpretation of component assembly actions.
机译：本文提出了一种视觉跟踪和分析方法，用于自动监视工业手动装配过程，其中，每个工人在装配过程中都从不同的盒子中顺序拾取零件。对装配过程的自动监视可以通过发出预警来减少装配错误。我们提出了一种手部跟踪和轨迹分析方法，该方法可从由多个未经校准的具有重叠视图的摄像机捕获的视频中获取。所提出的方法由以下三个模块组成：单视图手部跟踪，跨视图的一致标记以及根据多视图时间动态的最佳决策。该论文的主要新颖之处包括：（a）通过应用K均值聚类来适应多个实例的目标模型学习，以适应不同水平的光反射； （b）基于对称对极距离的最佳标准，以便在视图之间一致地标记被追踪的手； （c）通过将对极线与先前跟踪的结果相结合来对错误检测进行后向校正； （d）一种用于使用二进制手位置图分析手轨迹的多视图投票方案。已经通过多个未经校准的摄像机对视频进行了实验，其中一个人执行组装操作。测试结果和性能评估已经证明了该方法的有效性，该方法在多角度一致地估计手部轨迹和准确地解释组件装配动作方面。

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