An adaptive color-based particle filter
An adaptive color-based particle filter
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DOI：
10.1016/s0262-8856(02)00129-4
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发表时间：
2003-01-10
影响因子：
4.7
通讯作者：
Van Gool, L
中科院分区：
文献类型：
--
作者：
Nummiaro, K;Koller-Meier, E;Van Gool, L
对非刚性物体的鲁棒实时跟踪是一项具有挑战性的任务。粒子滤波已被证明对于非线性和非高斯估计问题非常成功。本文介绍了将颜色分布集成到粒子滤波中，粒子滤波通常与基于边缘的图像特征结合使用。应用颜色分布，因为它们对部分遮挡是鲁棒的，是旋转和尺度不变的，并且计算效率高。由于对象的颜色可以根据照明、视角和相机参数随时间变化，因此在时间稳定的图像观察期间调整目标模型。由于跟踪对象可能消失和重新出现，因此引入了基于外观条件的初始化。通过与均值漂移跟踪器的比较以及均值漂移跟踪器与卡尔曼滤波器的结合，说明了新方法的优点和局限性。(C)2002 Elsevier Science B.V.保留所有权利。
Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Color distributions are applied, as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. As the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters, the target model is adapted during temporally stable image observations. An initialization based on an appearance condition is introduced since tracked objects may disappear and reappear. Comparisons with the mean shift tracker and a combination between the mean shift tracker and Kalman filtering show the advantages and limitations of the new approach. (C) 2002 Elsevier Science B.V. All rights reserved.