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摘要: 视频目标跟踪在计算机视觉领域有着广泛应用，由于目标自身和外界环境变化的复杂性和难以预知性，使得复杂场景下鲁棒实时目标跟踪成为一项亟待解决的关键问题.由于视觉信息可以用少量神经元进行稀疏表示，因此稀疏表示已经广泛应用于人脸识别、目标检测和目标跟踪等计算机视觉领域.本文旨在对基于稀疏表示的视频目标跟踪算法进行综述.首先，介绍了基于稀疏表示的视频目标跟踪算法中的字典组成；其次，介绍了稀疏模型的构建及求解算法和模型更新，并对算法复杂度进行了简要分析；然后，对现有公开代码的稀疏表示跟踪算法在测试数据上进行了实验分析，结合算法模型和实验结果对其进行了分析；最后，对基于稀疏表示的视频跟踪算法存在问题进行了讨论，并对未来的研究趋势进行了展望.Abstract: Visual object tracking has been widely used in computer vision. Due to the complexity and unpredictability of the object itself and surroundings' changes, robust and real-time tracking is a key issue in urgent need of settlement in complex scenes. Since vision information can be expressed by few neurons, sparse representation has already been used in face recognition, object detection, visual tracking and so on. This paper aims to review the state-of-the-art of sparse representation-based visual tracking algorithms. Firstly, we introduce the codebook employed in the sparse representation-based trackers. Secondly, sparse model construction, corresponding solution and model update are described. And the algorithm complexity is briefly analyzed. Thirdly, the open sparse representation-based trackers' codes are conducted on the benchmark datasets, and the experimental results are fully analyzed in combination with models. Finally, we discuss the existing problems of sparse representation-based trackers and prospect the future.
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Key words:
- Visual tracking /
- sparse representation /
- algorithm evaluation /
- experiment analysis
1) 本文责任编委 桑农 -
图 4 算法跟踪精度随中心误差阈值的变化曲线
Fig. 4 The tracker$'$s tracking precision versus center error threshold
图 5 算法跟踪成功率随重叠率阈值的变化曲线
Fig. 5 The tracker$'$s success rate versus overlap rate threshold
表 1 视频跟踪评估基准数据
Table 1 Summary of some visual tracking evaluation benchmark datasets
数据集 时间 视频数量 地址 VOT 2013 2013 16 http://www.votchallenge.net/vot2013/ OTB 50 2013 50 http://visualtracking.net PTB 2013 100 http://tracking.cs.prince-ton.edu/submit.php ALOV++ 2013 315 http://www.alov300.org/ VOT 2014 2014 25 http://www.votchallenge.net/vot2014/ OTB 100 2015 100 http://pami.visual-tracking.net/ NUS-PRO 2015 365 http://www.lv-nus.org/pro/nus_pro.html 
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表 2 算法简称和论文代码地址
Table 2 The sparse tracker$'$s abbreviation, paper and code$'$s URL
算法 论文题目和代码地址 L$_1$ Robust visual tracking using l1 mimization. In ICCV, 2009.
http://www.dabi.temple.edu/~hbling/publication-selected.htmLSK Robust tracking using local sparse appearance model and k-selection. In CVPR, 2011.
http://www.uky.edu/~lya227/spt.htmlL1APG Real time robust l1 tracker using accelerated proximal gradient approach. In CVPR, 2012.
http://www.dabi.temple.edu/~hbling/publication-selected.htmASLA Visual tracking via adaptive structural local sparse appearance model. In CVPR, 2012.
http://ice.dlut.edu.cn/lu/publications.htmlSCM Robust object tracking via sparsity-based collaborative model. In CVPR, 2012.
http://ice.dlut.edu.cn/lu/publications.htmlMTT Robust visual tracking via multi-task sparse learning. In CVPR, 2012.
http://faculty.ucmerced.edu/mhyang/pubs.htmlLRT Low-rank sparse learning for robust visual tracking. In ECCV, 2012.
http://faculty.ucmerced.edu/mhyang/pubs.htmlCT Real-time compressive tracking. In ECCV, 2012.
http://www4.comp.polyu.edu.hk/~cslzhang/papers.htmDLSR Online discriminative object tracking with local sparse representation. In WACV, 2012.
http://faculty.ucmerced.edu/mhyang/pubs.htmlSRPCA Online object tracking with sparse prototypes. In TIP, 2013.
http://ice.dlut.edu.cn/lu/publications.htmlDSSM Visual trcking via discriminative sparse similiarity map. In TIP, 2014.
http://ice.dlut.edu.cn/lu/publications.htmlSST Structural sparse tracking. In CVPR, 2015.
http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/index.htmlCST In defense of sparse tracking: Circulant sparse tracker. In CVPR, 2016
http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/index.html
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表 3 算法跟踪成功率(%)比较
Table 3 The tracker$'$s success rate (%) comparison
算法 ASLA SCM CST SST LRT LSK MTT DSSM CT L1APG L$_1$ DLSR SRPCA 成功率 70.69 68.96 68.20 59.30 59.02 56.09 54.98 45.80 42.29 41.61 35.56 34.22 25.83
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表 4 算法单帧平均处理时间比较(ms)
Table 4 The comparison of tracker$'$s average processing time (ms)
算法 ASLA SCM CST SST LRT LSK MTT DSSM CT L1APG L$_1$ DLSR SRPCA 实现环境 MC MC M M M ME M M MC MC MC MC MC 时间 241 7846 454 450 3152 382 2279 586 12 79 397 23030 249
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表 5 基于稀疏表示的视频跟踪算法模型和重叠率比较
Table 5 The comparison of the sparse representation-based tracker$'$s model and overlap rate mean and std
算法简称 特征字典 运动模型 搜索方案 匹配模式 模型更新 重叠率均值 重叠率标准差 ASLA 局部灰度 仿射运动 粒子滤波 生成式 增量学习 0.5860 0.3225 SCM 局部灰度 仿射运动 粒子滤波 混合式 模板替换 0.5562 0.3436 CST HOG 仿射运动 粒子滤波 生成式 模板替换 0.5480 0.3063 LRT 全局灰度 仿射运动 粒子滤波 判别式 模板替换 0.4841 0.3247 SST 局部灰度 仿射运动 粒子滤波 生成式 模板替换 0.4840 0.3187 LSK 局部灰度 相似性变换 均值漂移 生成式 加权更新 0.4801 0.3379 MTT 全局灰度 仿射运动 粒子滤波 生成式 模板替换 0.4623 0.3411 CT 扩展类haar 平移运动 稠密采样 判别式 Bayes更新 0.3909 0.2850 DSSM 全局灰度 仿射运动 粒子滤波 判别式 模板替换 0.3818 0.3520 L1APG 全局灰度 仿射运动 粒子滤波 生成式 模板替换 0.3660 0.3565 DLSR 局部灰度 仿射运动 粒子滤波 判别式 SVM更新 0.3116 0.3414 L$_1$ 全局灰度 仿射运动 粒子滤波 生成式 模板替换 0.3068 0.3687 SRPCA 全局PCA 仿射运动 粒子滤波 生成式 增量学习 0.2412 0.3321
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