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Entropy Minimization Based Multi Object Tracking

机译:基于熵最小化的多目标跟踪

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摘要

Tracking in image sequences finds its application in many fields including that of surveillance. Object tracking in such sequences is however challenging due various reasons like presence of noise, similarity of the objects, resolution of the images etc. The paper evaluates entropy minimization based technique for tracking on various data sequences to show that it gives accurate results even in the presence of illumination changes, occlusion and drastic motion of the camera that is common in image sequences with moving camera. Multi object tracking has been implemented using this approach and tested for efficiency on various benchmark sequences.
机译:在图像序列中进行跟踪可在许多领域找到应用,包括监视领域。然而,由于各种原因(例如噪声的存在,对象的相似性,图像的分辨率等),以这种顺序进行对象跟踪非常具有挑战性。本文评估了基于熵最小化的技术来跟踪各种数据序列,以表明即使在图像序列中也能给出准确的结果相机出现照明变化,遮挡和剧烈运动的情况,这在移动相机的图像序列中很常见。使用此方法已实现了多对象跟踪,并在各种基准序列上进行了效率测试。

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