结合ORB特征和色彩模型的视觉跟踪算法

摘要

为解决CAMShift算法在色彩相似背景下跟踪失效的问题，提出一种结合ORB特征点和目标色彩模型的视觉跟踪算法。运用ORB特征匹配检测目标的初始位置，提出自适应的色彩分割阈值算法以提高目标的色彩模型精度，并在跟踪过程中通过ORB特征点包含信息对搜索窗口进行修正。然后对目标的丢失增加判断方法，并且建立迭代更新的特征模板用于重新定位丢失目标。实验结果证明，与CAMShift算法和基于特征提取的同类改进算法相比，该算法在目标快速运动场景下的跟踪具有较好的鲁棒性，能够对错误的跟踪结果进行判断并修正，并在计算效率上得到较大的提升。%To solve the problem of invalid tracking by traditional CAMShift owing to the background with similar colors, a dynamic visual tracking algorithm is proposed combining ORB feature and color model of the object. The ORB feature is applied to extract the initial position of the object, and a adaptive color-threshold segmentation algorithm is proposed to improve the accuracy of color model for the object. Besides, the information of ORB feature points is used to revise the search window in the tracking procedure, which improves the tracking accuracy and robustness. Furthermore, a new method is proposed to estimate whether the moving object is missing, and an iteratively updated feature template is built to relocate the disappeared target. The experiments on video sequence images demonstrate that the proposed algorithm outperforms CAMShift and other improved algorithms based on feature extraction. When the target moves at high speed, the proposed algorithm has good robustness and can find out the wrong tracking result and correct it. Moreover, the computational efficiency rises greatly to ensure the real-time performance.