A automatic face detection and tracking algorithm is presented, which is of very low computational complexity and is rather robust to image content.
给出了一种运算量低、鲁棒性好的人脸自动识别和跟踪算法。
With the use of a robust error mapping function, an affine model-based target tracking algorithm is formulated, in which a quasi-Newton's iterative method is implemented in the optimization progress.
在采用一个鲁棒性匹配误差映射函数的基础上，使用一种类牛顿法的迭代优化方法实现了基于仿射模型的图像目标跟踪算法。
Then, the original tracking based on two points' direction is extended to multi-point tracking, which makes the algorithm more robust for tracking the curving vessel.
其次，在跟踪方向的计算上，将原始的基于两点的跟踪方向扩展到多点的跟踪，使得算法对于弯曲血管的跟踪更加稳定。
Experimental results show that the lane detection and the moving object detection and tracking algorithm in this paper are robust, efficient and suitable for real-time processing.
实验结果表明，本文提出的车道检测和车辆识别与跟踪方法是可行的，能够满足系统实时性和准确性的要求。
Experimental results show that the lane detection and the moving object detection and tracking algorithm in this paper are robust, efficient and suitable for real-time processing.
实验结果表明，本文提出的车道检测和车辆识别与跟踪方法是可行的，能够满足系统实时性和准确性的要求。
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