基于快速鲁棒特征的CamShift跟踪算法

为了解决CamShift算法由于对颜色敏感导致的跟踪效果变差或失效的问题，提出一种基于局部特征匹配的CamShift跟踪算法。采用快速鲁棒特征(SURF)方法在多通道图像的目标区域和搜索区域提取包含图像信息的局部特征点，并利用近似最近邻搜索对特征点进行匹配;使用提纯后的匹配结果得到特征点的位置、尺度及方向信息，对CamShift方法进行约束和更新，以提高跟踪精度和稳定性。实验结果表明，与经典CamShift算法和同类的改进算法相比，该算法能够较好地实现对复杂背景下旋转和放缩运动目标的实时跟踪。

CamShift tracking algorithm based on speed-up robust features

In order to deal with the poor or invalid tracking performance caused by the color sensitivity of Continuously adaptive Mean Shift (CamShift) algorithm, a new CamShift tracking algorithm based on local feature matching was proposed. The new algorithm used the method of Speeded-Up Robust Feature (SURF) to extract the local feature points containing the image information from the target and searched areas of multi-channel images, and then matched the feature points by the method of approximate nearest neighbor searching. The location, scale and orientation information of the feature points were obtained utilizing the purified matching results, therefore the CamShift method was constrained and updated to improve the accuracy and stability of tracking. The experimental results show that the new algorithm can outperform the classic CamShift algorithm and the similar improved algorithms for rotating and zooming objects against complex backgrounds in real-time tracking.