论文标题
具有分级颜色名称功能的实时视觉跟踪
Towards Real-Time Visual Tracking with Graded Color-names Features
论文作者
论文摘要
由于其简单性和效率，卑鄙的速度算法已被广泛用于跟踪任务。但是，传统的刻痕算法需要标记目标的初始区域，从而降低了算法的适用性。此外，它仅适用于目标区域和候选区域之间的重叠率较高的现场。因此，当目标速度快速时，目标尺度变化，形状变形或目标闭塞会发生，跟踪性能将恶化。在本文中，我们通过开发一种跟踪方法来解决上述挑战，该方法结合了刻薄框架下的背景模型和颜色名称的分级特征。在上述情况下，此方法可显着提高性能。此外，它有助于检测准确性和检测速度之间的平衡。实验结果证明了该方法的验证。
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.
