In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.

中文翻译：

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基于混合滤波和多特征融合的Camshift目标跟踪算法的实现

近年来，均值平移算法在视频跟踪领域具有广泛的应用。它具有成本低，存储空间小，跟踪效果好等优点。但是，现有算法存在一些不足。例如，它无法随着目标大小的变化而产生自适应变化。并且当存在类似物体时，很容易发生目标定位错误和由于遮挡而导致的跟踪失败。提出了一种改进的连续自适应变化均值平移（Camshift）用于高精度定位和跟踪的方法。传统的Camshift方法仅使用HSV中的色相分量来提取特征。本文使用HSV空间中H和S分量的组合来构建二维颜色特征直方图，并使用图像的LBP特征直方图来提高跟踪精度。同时，出于目标遮挡和跟踪过程中非线性变化的原因，本文介绍了一种由卡尔曼滤波器更新的高斯-赫米特粒子滤波器。实验结果表明，该方案的实时性能优于均值漂移，Camshift，简单粒子滤波和卡尔曼滤波。