目的 提出一种鲁棒的目标跟踪算法，将区别性稀疏表示模型应用于残差Unscented粒子滤波（RUPF）跟踪框架，从而实现对目标高效准确的跟踪。方法 利用Unscented卡尔曼（UKF）滤波技术将目标的量测信息引入提议分布，并使用马尔可夫蒙特卡洛（MCMC）移动改进采样结果，提高了滤波的精度，同时有效防止了粒子的退化和贫化。基于稀疏表示建立区别性的目标观测模型，引入的背景成分可以增强算法分辨目标与背景的能力。采用可变方向乘子法（ADMM）解决稀疏表示中的L1优化问题，有效地提升了算法的执行效率。结果 通过和其他跟踪算法一起，对标准测试视频进行的大量定性与定量的实验，结果表明，本文跟踪算法的跟踪精度高于一些常见的跟踪算法，同时其时间复杂度低于传统的几种基于稀疏的跟踪算法。结论 随着硬件技术的不断发展，UKF滤波技术的速度不断提升，保证了本文算法可以在较高准确率下有更快的执行速度。

Objective A robust tracking approach based on residual unscented particle filter (RUPF) and discriminative sparse representation is proposed to track an object accurately and efficiently. Method The Unscented Kalman filter is used to bring the target's observation into its proposal distribution. Then the Markov chain Monte Carlo (MCMC) improves the sampling result. The accuracy of filtering is enhanced and problems such as particle degeneration and dilution can be restrained with our RUPF. Observation likelihood is modeled based on the discriminative sparse representation, which improves the ability to extract the target from the background. The L1-regularized least squares problem in sparse representation is solved using the alternating direction method of multipliers (ADMM). Result Both quantitative and qualitative experiments are conducted on several challenging image sequences and the comparisons with other state-of-the-art trackers demonstrate that our tracker is more accurate than some common trackers and owns less computational complexity than traditional sparse-based trackers. Conclusion With the development of the hardware, a quickly operated UKF guarantees a faster speed of our tracker with a high tracking accuracy.