| 孙锐,王旭,张东东,高隽.采用局部线性嵌入的稀疏目标跟踪方法[J].电子测量与仪器学报,2017,31(8):1312-1320 |
| 采用局部线性嵌入的稀疏目标跟踪方法 |
| Sparse object tracking method using local linear embedding |
| |
| DOI：10.13382/j.jemi.2017.08.021 |
| 中文关键词: 外观模型 目标跟踪 局部线性嵌入 稀疏表示 流形学习 |
| 英文关键词:appearance model object tracking local linear embedding (LLE) sparse representation manifold learning |
| 基金项目:国家自然科学基金（61471154）资助项目 |
|
|
| Author | Institution |
| Sun Rui | School of Computer and Information, Hefei University of Technology, Hefei 230009, China |
| Wang Xu | School of Computer and Information, Hefei University of Technology, Hefei 230009, China |
| Zhang Dongdong | School of Computer and Information, Hefei University of Technology, Hefei 230009, China |
| Gao Jun | School of Computer and Information, Hefei University of Technology, Hefei 230009, China |
|
| 摘要点击次数: 2685 |
| 全文下载次数: 15068 |
| 中文摘要: |
| 目标跟踪是从复杂的背景中辨认出运动目标，并且对目标进行准确且连续的追踪。如何在遮挡、形变、背景复杂的条件下鲁棒性跟踪目标仍是亟待解决的问题。针对遮挡和形变问题，提出一种局部线性嵌入（LLE）和稀疏表示的算法来有效的学习外观模板。其中LLE是流形学习的一种典型算法。在该算法中每个点的近邻权值在平移、旋转、伸缩变化下是保持不变的，因此可以用来提取目标的本质特征，发现数据的内在规律。算法首先采用局部线性嵌入提取低维特征，提取后的特征作为基向量与琐碎模板组成稀疏原型，稀疏原型用于模板的更新。算法保持了原有稀疏跟踪方法对遮挡处理的优势，同时对目标形变有较好的稳健性。 实验结果表明，跟踪算法比其他7个常用的算法在9个视频序列中有较好的鲁棒性能。 |
| 英文摘要: |
| Definition of object tracking is identifying the moving targets from complex background, and it should track the target accurately and continuously. In occlusion, deformation, complex background conditions robust tracking target is still a challenging problem to be solved. A novel online object tracking algorithm is proposed for the occlusion and deformation with sparse prototypes, which exploits local linear embedding (LLE) algorithm with sparse representation scheme for learning effective appearance model. LLE is a classic manifold learning algorithm. In the algorithm, the neighbor points weight of each point remains unchanged in translation, rotation, scale changes. Thus, it can be used to extract the essential characteristics of target and find the inherent law of data. Firstly, the algorithm uses the local linear embedding algorithm to extract low dimensional characteristic. Then the sparse prototype is composed of the base vector which is extracted from the low dimensional characteristic and trivial templates. It can be used to update templates. This algorithm maintains the advantages of the original sparse tracking method to occlusion, and has a good robust tracking effect of deformation object. The experimental results show that the proposed tracking algorithm is better than the other seven commonly used algorithms in the nine video sequences. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
