提出了一种包含随机运动的复杂密集场景下的目标跟踪方法. 在跟踪算法中，将稀疏模型与多模块彩色特征相结合，并通过将其转化为l1正则化最小二乘问题实现对特征的稀疏投影. 跟踪过程中利用粒子滤波得到预测跟踪点，并将对应于最小投影差的预测点模块作为最优跟踪. 为适应特征变化，在跟踪完成后根据新的跟踪结果自动更新目标模板. 大量包含遮挡和光照变化的不同类型密集场景测试验证了该方法的有效性，与其他算法的比较说明了其优良性能.

 This paper presents a target tracking framework applicable to complex crowded scenes with random movements. A robust tracking algorithm using a local sparse appearance model associated with a multi-part color representation is proposed. Sparsity is achieved by solving an l1 regularized least squares problem.Candidates with the smallest projection error is taken as the tracking result. All candidates are drawn based on a density distribution in a Bayesian state inference framework. The target templates are dynamically updated to adapt appearance variation at the end of a tracking iteration. We test the approach on numerous videos including different type of very crowded scenes with serious occlusion and illumination variation. The proposed approach demonstrates excellent performance in comparison with previous methods.