1. 毕业设计（论文）主要目标：
研究基于鲁棒PCA的运动目标检测算法，并对鲁棒PCA目标检测算法进行优化，提取图像中的运动目标；通过对多种算法进行实验、评估、分析并综合比较各种算法的优劣。
2. 毕业设计（论文）主要内容：
本毕业论文主要研究基于鲁棒PCA的运动目标检测 ，具体内容包括：（1）综述前景运动目标检测的研究现状；（2）分析鲁棒PCA的基本原理，研究基于鲁棒PCA的运动目标检测算法；（3）建立鲁棒PCA目标检测的优化算法，提取各帧图像中的运动目标；（4）对多种算法进行实验评估与分析；
3. 主要参考文献
（1）Bredies, K. and Lorenz, D. A. Iterated hard shrinkage for minimization problems with sparsity constraints. SIAM Journal on Scientific Computing, 30(2):657–683, 2008.（2）Cai, J., Candes, E. J., and Shen, Z. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization,20(4):1956–1982, 2010.（3）Candes, E. J. and Tao, T. The power of convex relaxation: Near- optimal matrix completion. arXiv: 0903.1476, 2009.（4）Candes, E. J., Li, X., Ma, Y., and Wright, J. Robust principal `component analysis? Journal of the ACM (submitted), 2009.（5）Chandrasekaran, Venkat, Sanghavi, Sujay, Parrilo, Pablo A, and Willsky, Alan S. Rank-sparsity incoherence for matrix decomposition.arXiv:0906.2220, 2009.（6）Cheng, L., Gong, M., Schuurmans, D., and Caelli, T. Realtime discriminative background subtraction. to appear in IEEE Trans on Image Processing, 2010.（7）Donoho, D. L. Compressed sensing. IEEE Trans on Information Theory, 52(4):1289–1306, 2006.（8）Fazel, M., Candes, E. J., Recht, B., and Parrilo, P. Com- `pressed sensing and robust recovery of low rank matrices. In 42nd Asilomar Conference on Signals, Systems and Computers,2008.（9）Halko, N., Martinsson, P. G., and Tropp, J. A. Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions. arXiv: 0909.4061,2009.（10）Keshavan, R. and Oh, S. Optspace: A gradient descent algorithm on grassman manifold for matrix completion. Submitted to IEEE Trans on Signal Processing, 2009.（11）Lewis, A. S. and Malick, J. Alternating projections on manifolds.Mathematics of Operations Research, 33(1):216–234, 2008.Roweis, S. Em algorithms for pca and spca. In NIPS, pp. 626–632, 1998.（12）Zhou, T. and Tao, D. Bilateral random projection based low-rank approximation. Technical report, 2010.（13）Zhou, T., Tao, D., and Wu, X. Manifold elastic net: a unified framework for sparse dimension reduction. Data Mining andKnowledge Discovery (Springer), 22(3):340–371, 2011.（14）Zhou, Z., Li, X., Wright, J., Candes, E. J., and Ma, Y. Stable `principal component pursuit. In ISIT, 2010.
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