中文摘要：

目的L1跟踪对局部遮挡具有良好的鲁棒性，但存在易产生模型漂移和计算速度慢的问题。针对这两个问题，该文提出了一种基于判别稀疏表示的视觉跟踪方法。方法考虑到背景和遮挡信息的干扰，提出了一种判别稀疏表示模型，并基于块坐标优化原理，采用学习迭代收缩阈值算法和软阈值操作设计出了表示模型的快速求解算法。结果在8组图像序列中，该文方法与现有的4种经典跟踪方法分别在鲁棒性和稀疏表示的计算时间方面进行了比较。在鲁棒性的定性和定量比较实验中，该文方法不仅表现出了对跟踪过程中的多种干扰因素具有良好的适应能力，而且在位置误差阈值从0～50像素的变化过程中，其精度曲线均优于实验中的其他方法；在稀疏表示的计算时间方面，在采用大小为16×16和32×32的模板进行跟踪时，该文算法的时间消耗分别为0．152s和0．257s，其时效性明显优于实验中的其他方法。结论与经典的跟踪方法相比，该文方法能够在克服遮挡、背景干扰和外观改变等诸多不良因素的同时，实现快速目标跟踪。由于该文方法不仅具有较优的稀疏表示计算速度，而且能够克服多种影响跟踪鲁棒性的干扰因素，因此可以将其应用于视频监控和体育竞技等实际场合。

英文摘要：

Objective Visual tracking is an important field in computer vision and is applied in various domains. Although numerous visual tracking methods have been developed in the past several decades, many challenging issues （e. g. , occlu- sions, illumination changes, and background clutter） still affect the tracking performance of these methods. Inspired by sparse representation applied in face recognition, the LI tracker based on sparse representation was proposed by Mei et al. The L1 tracker has good robustness toward partial occlusion but is prone to model drift and time consuming. To address these two problems, this study proposes a tracking method based on discriminative sparse representation. Method Consid- ering the interference of background and occlusion information, a discriminative sparse representation model is proposed. The proposed model uses the sparseness of the coefficients associated with target and background templates so that the can- didate targets can be represented accurately. The sparseness of the coefficients associated with trivial templates makes the proposed tracker robust to partial occlusion. By using the coefficients associated with trivial and target templates, the obser-vation likelihood model, which is adopted in this study, eliminates the interference of the background information and leads to improved tracking results. A fast sparse representation algorithm is designed to increase the tracking speed and used to calculate the coefficients of the discriminative sparse representation model. At the first stage, the proposed algorithm uses the learned iterative shrinkage and thresholding algorithm （LISTA） to calculate the coefficients associated with target tem- plates. At the second stage, the proposed algorithm uses the soft shrinkage operator to calculate the coefficients associated with trivial templates. Based on block coordinate optimization theory, the above optimization procedure is iteratively used to obtain excellent sparse representation coefficients. Under the particle