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首页> 外文期刊>Journal of VLSI signal processing systems >A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity
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A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity

机译：基于鲁棒粒子滤波的动态物体形状和外观相似度跟踪单个视觉物体通过复杂场景的方法

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摘要

This paper addresses the issue of tracking a single visual object through crowded scenarios, where a target object may be intersected or partially occluded by other objects for a long duration, experience severe deformation and pose changes, and different motion speed in cluttered background. A robust visual object tracking scheme is proposed that exploits the dynamics of object shape and appearance similarity. The method uses a particle filter where a multi-mode anisotropic mean shift is embedded to improve the initial particles. Comparing with the conventional particle filter and mean shift-based tracking (Shan et al. 2004), our method offers the following novelties: We employ a fully tunable rectangular bounding box described by five parameters (2D central location, width, height, and orientation) and full functionaries in the joint tracking scheme; We derive the equations for the multi-mode version of the anisotropic mean shift where the rectangular bounding box is partitioned into concentric areas, allowing better tracking objects with multiple modes. The bounding box parameters are then computed by using eigen-decomposition of mean shift estimates and weighted averaging. This enables a more efficient redistributions of initial particles towards locations associated with large weights, hence an efficient particle filter tracking using a very small number of particles (N = 15 is used). Experiments have been conducted on video containing a range of complex scenarios, where tracking results are further evaluated by using two objective criteria and compared with two existing tracking methods. Our results have shown that the propose method is robust in terms of tracking drift, tightness and accuracy of tracked bounding boxes, especially in scenarios where the target object contains long-term partial occlusions, intersections, severe deformation, pose changes, or cluttered background with similar color distributions.
机译：本文解决了在拥挤的场景中跟踪单个视觉对象的问题，在该场景中，目标对象可能长时间与其他对象相交或部分被其他对象遮挡，经历严重的变形和姿势变化，并且在杂乱的背景中出现不同的运动速度。提出了一种鲁棒的视觉对象跟踪方案，该方案利用了对象形状和外观相似性的动态特性。该方法使用粒子滤波器，其中嵌入了多模式各向异性均值偏移以改善初始粒子。与传统的粒子滤波器和基于均值漂移的跟踪相比（Shan等，2004），我们的方法具有以下新颖性：我们采用了完全可调的矩形边界框，由五个参数（二维中心位置，宽度，高度和方向）描述）和全员参与联合跟踪计划；我们导出了各向异性均值平移的多模式版本的方程，其中矩形边界框被划分为同心区域，从而可以更好地跟踪具有多个模式的对象。然后，通过使用均值漂移估计值的特征分解和加权平均来计算边界框参数。这可以使初始粒子更有效地重新分配给与较大权重关联的位置，因此可以使用很少的粒子（使用N = 15）进行有效的粒子过滤器跟踪。已经对包含一系列复杂场景的视频进行了实验，其中使用两个客观标准进一步评估了跟踪结果，并与两种现有的跟踪方法进行了比较。我们的结果表明，该方法在跟踪漂移，紧密度和跟踪边界框的准确性方面都非常可靠，尤其是在目标对象包含长期局部遮挡，相交，严重变形，姿势变化或背景杂乱的情况下。类似的颜色分布。

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