MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate between the target area and the candidate area. Therefore, when the target speed is fast, the target scale change, shape deformation or the target occlusion occurs, the tracking performance will be deteriorated. In this paper, we address the challenges above-mentioned by developing a tracking method that combines the background models and the graded features of color-names under the MeanShift framework. This method significantly improve performance in the above scenarios. In addition, it facilitates the balance between detection accuracy and detection speed. Experimental results demonstrate the validation of the proposed method.
翻译：在跟踪任务时,由于程序简单、效率高,传统Spoin Shift算法被广泛使用。然而,传统的Spoen Shift算法需要给目标的初始区域贴上标签,从而减少了算法的适用性。此外,它只适用于目标区域和候选区域之间有很大重叠率的现场。因此,当目标速度快时,目标规模变化、形状变形或目标隔离发生时,跟踪性能将会恶化。在本文件中,我们通过开发一种跟踪方法来应对上述挑战,该方法结合了MayShift框架下的背景模型和颜色名称的等级特征。这种方法大大改进了上述情景的性能。此外,它促进了探测准确性和探测速度之间的平衡。实验结果显示了对拟议方法的验证。
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