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Fast incorporation of optical flow into active polygons

机译:快速将光流纳入活动多边形

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In this paper, we first reconsider, in a different light, the addition of a prediction step to active contour-based visual tracking using an optical flow and clarify the local computation of the latter along the boundaries of continuous active contours with appropriate regularizers. We subsequently detail our contribution of computing an optical flow-based prediction step directly from the parameters of an active polygon, and of exploiting it in object tracking. This is in contrast to an explicitly separate computation of the optical flow and its ad hoc application. It also provides an inherent regularization effect resulting from integrating measurements along polygon edges. As a result, we completely avoid the need of adding ad hoc regularizing terms to the optical flow computations, and the inevitably arbitrary associated weighting parameters. This direct integration of optical flow into the active polygon framework distinguishes this technique from most previous contour-based approaches, where regularization terms are theoretically, as well as practically, essential. The greater robustness and speed due to a reduced number of parameters of this technique are additional and appealing features.
机译:在本文中,我们首先在不同的光线下重新考虑使用光流向基于活动轮廓的视觉跟踪添加预测步骤,并使用适当的规则化器沿连续活动轮廓的边界阐明后者的局部计算。随后,我们将详细说明我们直接从活动多边形的参数计算基于光流的预测步骤以及在对象跟踪中利用它的贡献。这与光流及其临时应用的明确分开的计算相反。它还提供了由于沿多边形边缘对测量值进行积分而产生的固有正则化效果。结果,我们完全避免了在光流计算中添加临时正则项以及不可避免地任意关联的加权参数的需求。这种将光流直接集成到活动多边形框架中的方法使该技术与大多数以前的基于轮廓的方法不同,后者在理论上和实践上都必须使用正则化项。由于减少了此技术的参数数量,因此具有更高的鲁棒性和速度,这是其他吸引人的功能。

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