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首页> 外文会议>International conference on image processing, computer vision, pattern recognition >Adaptive Covariance Tracking with Clustering-based Model Update
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Adaptive Covariance Tracking with Clustering-based Model Update

机译：基于聚类的模型更新的自适应协方差跟踪

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摘要

We propose a novel approach to track nonrigid objects using the recently proposed adaptive covariance descriptor [1] with clustering-based model update mechanism. The adaptive covariance descriptor represents an object of interest according to its characteristics in a small-dimensional covariance matrix and possesses higher discriminative power with respect to the original covariance descriptor. A clustering-based update mechanism is then conducted on the target model to adapt to the object appearance changes during the tracking process. We show that by updating with a carefully selected cluster, the update mechanism can efficiently deal with significant appearance deformations and severe occlusions. Comparative experimental results on challenging video sequences demonstrate the effectiveness of the proposed approach.
机译：我们提出了一种新方法来跟踪非刚性对象，该方法使用了最近提出的带有基于聚类的模型更新机制的自适应协方差描述符[1]。自适应协方差描述符根据其在小维协方差矩阵中的特征表示感兴趣的对象，并且相对于原始协方差描述符具有较高的判别力。然后在目标模型上执行基于聚类的更新机制，以适应跟踪过程中对象外观的变化。我们显示，通过使用精心选择的群集进行更新，更新机制可以有效地处理重大的外观变形和严重的遮挡。具有挑战性的视频序列的比较实验结果证明了该方法的有效性。

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