针对智能监控的行人轮廓特征提取，先利用MDL shape model得到紧致但能充分表征轮廓几何特征标志点之间的对应关系，再利用椭圆傅里叶分解把行人轮廓表示为一系列不同频率下椭圆傅里叶系数组成的向量，最后得到行人2D＋time轮廓的描述向量。实验结果表明，此方法不但可以在几何上直观地表示行人轮廓，而且大大降低了轮廓向量的维数。

To extract the contour features of pedestrian under surveillance, this paper applies Minimum Description Length Shape to get compact point correspondences, which can capture important shape characteristics, of 2D+time contour, and then uses Elliptic Fourier Decomposition to a series of EFT coefficients at different frequency level to compose a contour vector. Experiments indicate our method is efficient in contour representation and vector dimension reduction.