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首页> 外文会议>International conference on graphic and image processing >Multiple Feature Fusion via Covariance Matrix for Visual Tracking
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Multiple Feature Fusion via Covariance Matrix for Visual Tracking

机译：通过协方差矩阵进行视觉跟踪的多特征融合

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摘要

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.
机译：针对视觉目标跟踪中动态场景复杂的问题，提出了一种基于协方差矩阵的多特征融合跟踪算法，以提高跟踪算法的鲁棒性。在量子遗传算法的框架中，本文使用区域协方差描述符来融合颜色，边缘和纹理特征。它还使用快速协方差交点算法来更新模型。区域协方差描述符的低维，量子遗传算法的快速收敛速度和强大的全局优化能力以及快速协方差交点算法的快速计算可提高融合，匹配和更新过程的计算效率，从而使该算法实现了快速有效的多特征融合跟踪。实验证明，该算法不仅可以实现快速，鲁棒的跟踪，而且可以有效地处理遮挡，旋转，变形，运动模糊等干扰。

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