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Recognizing human actions in videos acquired by uncalibrated moving cameras

机译:在未经校准的移动摄像机拍摄的视频中识别人为动作

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Most work in action recognition deals with sequences acquired by stationary cameras with fixed viewpoints. Due to the camera motion, the trajectories of the body parts contain not only the motion of the performing actor but also the motion of the camera. In addition to the camera motion, different viewpoints of the same action in different environments result in different trajectories, which can not be matched using standard approaches. In order to handle these problems, we propose to use the multi-view geometry between two actions. However, well known epipolar geometry of the static scenes where the cameras are stationary is not suitable for our task. Thus, we propose to extend the standard epipolar geometry to the geometry of dynamic scenes where the cameras are moving. We demonstrate the versatility of the proposed geometric approach for recognition of actions in a number of challenging sequences.
机译:动作识别中的大多数工作都是处理具有固定视点的固定摄像机获取的序列。由于摄像机的运动,身体部位的轨迹不仅包含表演演员的运动,而且还包含摄像机的运动。除了摄像机运动之外,在不同环境中同一动作的不同视点会导致轨迹不同,这是使用标准方法无法匹配的。为了解决这些问题,我们建议在两个动作之间使用多视图几何。但是,众所周知的静态场景的静态几何结构(相机静止不动)不适合我们的任务。因此,我们建议将标准对极几何形状扩展到相机移动的动态场景的几何形状。我们证明了所提出的几何方法在多种挑战性序列中识别动作的多功能性。

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