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Using a Non-prior Training Active Feature Model

机译:使用非事先培训主动特征模型

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This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.
机译:本文介绍了在非先前训练主动特征模型(NPT-AFM)框架下的光流量的特征点跟踪算法。 该算法主要侧重于可变形对象的分析,并提供实时,鲁棒跟踪。 所提出的对象跟踪过程可以分为两个步骤:(i)特征点的基于光学流量的跟踪和(ii)NPT-AFM用于鲁棒跟踪。 为了在对象跟踪中处理遮挡问题,估计对象内的特征点代替其传统有源轮廓模型(ACM)或活动形状模型(ASM)的形状边界,并且被更新为培训集的元素 AFM。 所提出的NPT-AFM框架使得能够跟踪复杂的背景中的遮挡物体。 实验结果表明,所提出的基于NPT-AFM的算法可以实时跟踪可变形物体。

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