基于局部特征和Mean Shift的目标跟踪算法研究

利用均值漂移进行目标跟踪的算法,在被跟踪目标出现旋转、尺度变化、噪声干扰等情况下,无法得到准确的跟踪结果。文中提出了基于当前流行目标跟踪算法和局部特征相结合的算法,基于局部特征-形状上下文(Shape Context)特征的Mean Shift目标跟踪算法。该算法首先提取目标的轮廓信息和特征,根据采样点之间位置和距离关系建立Shape Context直方图,最后所有点的Shape Context直方图构成了图像的Shape Context特征,最后根据Mean Shift算法进行跟踪。实验结果表明,该算法在跟踪目标出现尺度变化、旋转、噪声干扰和遮挡等情况下能够准确地跟踪物体,鲁棒性好。

Research on Object Tracking Method Based on Local Feature and Mean Shift

Accurate tracking results are unobtainable by the traditional algorithm of Mean Shift due to noise,rotation and changed scales of the target etc.A shape context tracking algorithm for Mean Shift objects is proposed by combining the current popular target tracking algorithm and local features.The contour information and features of shape context are extracted from the target.Then a shape context histogram is established according to the locations and spacing of sample points and the histograms of all the points constitutes the shape context feature of the image after which the Mean Shift algorithm is adopted for tracking.The experimental results show that the algorithm can accurately track the object with scale change,rotation,noise and is of good accuracy and robustness.