基于多特征Mean Shift的人脸跟踪算法

该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift跟踪算法，提出了基于多特征的Mean Shift人脸跟踪算法以解决Mean shift跟踪算法的鲁棒性问题。通过对LTP纹理特征的分析、研究，提出了一个LTP关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP纹理模型。在此基础上，提出一种基于LTP关键纹理特征和肤色特征的Mean Shift人脸跟踪算法，有效地解决了Mean Shift算法的鲁棒性问题。为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明，该文的算法在目标定位的准确性和跟踪性能上比Mean Shift算法均有明显的提高。

A Face Tracking Algorithm Based on Multiple Feature Mean Shift

In this paper, an improved Mean Shift face tracking algorithm based on Local Ternary Patterns (LTP) of texture and color features is proposed to improve the robustness of the Mean Shift algorithm. Based on the study of LTP texture features, an LTP key texture pattern is introduced to enhance the important features of an object and reduce the computational complexity of the LPT texture model. A multiple feature Mean Shift face tracking algorithm is then proposed based on the LTP key texture and complexion features, and the robustness of Mean Shift algorithm is significantly enhanced. Furthermore, in order to improve the tracking speed and robustness, the Kalman filter is introduced to predict the position of the object window. Experimental results show that compared with the original Mean Shift algorithm, the proposed multiple feature face tracking algorithm has significantly improved the tracking performance.