基于分层弹性运动分析的非刚体跟踪方法
采用时一空分层的弹性运动跟踪策略，提出了一种分析长时运动稳定结构与短时运动局部变化的非刚体运动跟踪方法．首先，基于序贯形状聚类的分段弹性运动跟踪模型，将整段图像序列分割成若干子段，并利用弹性运动分析方法得到子段内各帧边缘点的对应关系和各类的平均形状，获取短时局部运动变化细节．然后，通过基于贝叶斯网的整体搜索算法寻找时序上相邻聚类平均形状之间的对应关系，进而得到整段运动的公共形状，用于表示长时运动稳定结构．通过计算公共形状与各类平均形状之间的变形关系，可以建立各聚类平均形状之间的对应关系，实现分段运动的连接．本方法的特点是不依赖先验模型、通用性好、目标的描述能力强．实验表明，本方法与现有不依赖模...
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| Published in | 自动化学报 Vol. 41; no. 2; pp. 295 - 303 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
智能信息技术北京市重点实验室 北京 100081%清华大学计算机科学与技术系 北京100084
2015
北京理工大学计算机学院 北京 100081 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0254-4156 1874-1029 |
| DOI | 10.16383/j.aas.2015.c140375 |
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| Abstract | 采用时一空分层的弹性运动跟踪策略，提出了一种分析长时运动稳定结构与短时运动局部变化的非刚体运动跟踪方法．首先，基于序贯形状聚类的分段弹性运动跟踪模型，将整段图像序列分割成若干子段，并利用弹性运动分析方法得到子段内各帧边缘点的对应关系和各类的平均形状，获取短时局部运动变化细节．然后，通过基于贝叶斯网的整体搜索算法寻找时序上相邻聚类平均形状之间的对应关系，进而得到整段运动的公共形状，用于表示长时运动稳定结构．通过计算公共形状与各类平均形状之间的变形关系，可以建立各聚类平均形状之间的对应关系，实现分段运动的连接．本方法的特点是不依赖先验模型、通用性好、目标的描述能力强．实验表明，本方法与现有不依赖模型的方法相比，具有更好的长时稳定性和更高的跟踪精确度． |
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| AbstractList | 采用时–空分层的弹性运动跟踪策略，提出了一种分析长时运动稳定结构与短时运动局部变化的非刚体运动跟踪方法。首先，基于序贯形状聚类的分段弹性运动跟踪模型，将整段图像序列分割成若干子段，并利用弹性运动分析方法得到子段内各帧边缘点的对应关系和各类的平均形状，获取短时局部运动变化细节。然后，通过基于贝叶斯网的整体搜索算法寻找时序上相邻聚类平均形状之间的对应关系，进而得到整段运动的公共形状，用于表示长时运动稳定结构。通过计算公共形状与各类平均形状之间的变形关系，可以建立各聚类平均形状之间的对应关系，实现分段运动的连接。本方法的特点是不依赖先验模型、通用性好、目标的描述能力强。实验表明，本方法与现有不依赖模型的方法相比，具有更好的长时稳定性和更高的跟踪精确度。 采用时一空分层的弹性运动跟踪策略，提出了一种分析长时运动稳定结构与短时运动局部变化的非刚体运动跟踪方法．首先，基于序贯形状聚类的分段弹性运动跟踪模型，将整段图像序列分割成若干子段，并利用弹性运动分析方法得到子段内各帧边缘点的对应关系和各类的平均形状，获取短时局部运动变化细节．然后，通过基于贝叶斯网的整体搜索算法寻找时序上相邻聚类平均形状之间的对应关系，进而得到整段运动的公共形状，用于表示长时运动稳定结构．通过计算公共形状与各类平均形状之间的变形关系，可以建立各聚类平均形状之间的对应关系，实现分段运动的连接．本方法的特点是不依赖先验模型、通用性好、目标的描述能力强．实验表明，本方法与现有不依赖模型的方法相比，具有更好的长时稳定性和更高的跟踪精确度． |
| Abstract_FL | In this paper, we present a spatial-temporal layered elastic motion tracking method to estimate long-term stable structures and short-term local motions for non-rigid targets. First, the sequence is segmented into several pieces by sequential shape clustering based a piece-wise elastic motion tracking model, the correspondence among frames in the same segment and the mean shape of all clusters are calculated by piece-wise elastic motion tracking. Then, we use a Bayesian network based global search method to find the correspondence of mean shapes of adjoining clusters and extract the common shape of the entire sequence. The proposed method, which does not require prior shape models, is adaptive to and descriptive for general objects. The experiments on non-rigid targets validate both the long-term stability and the detailed accuracy of our proposed method. |
| Author | 吕峰 邸慧军 陆耀 徐光祜 |
| AuthorAffiliation | 北京理工大学计算机学院,北京100081 智能信息技术北京市重点实验室,北京100081 清华大学计算机科学与技术系,北京100084 |
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| Author_FL | LV Feng LU Yao XU Guang-You DI Hui-Jun |
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| DocumentTitleAlternate | Non-rigid Tracking Method Based on Layered Elastic Motion Analysis |
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| Keywords | 贝叶斯网络 序贯形状聚类 运动分段 运动连接 motion segmentation sequential shape clustering 分层弹性运动跟踪 Bayesian network Layered elastic motion tracking motion connection |
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| Notes | LV Feng DI Hui-Jun LU Yao XU Guang-You （1. School of Computer Science, Beijing Institute of Technology, Beijing 100081 2. Beijing Laboratory of Intelligent Informa- tion Technology, Beijing 100081 3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084） Layered elastic motion tracking, sequential shape clustering, motion segmentation, motion connection,Bayesian network In this paper, we present a spatial-temporal layered elastic motion tracking method to estimate long-term stable structures and short-term local motions for non-rigid targets. First, the sequence is segmented into several pieces by sequential shape clustering based a piece-wise elastic motion tracking model, the correspondence among frames in the same segment and the mean shape of all clusters are calculated by piece-wise elastic motion tracking. Then, we use a Bayesian network based global search method to find the correspondence of mean shapes of adjoining clusters and extract the common shape of the entire sequenc |
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| PublicationTitle | 自动化学报 |
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| Publisher | 智能信息技术北京市重点实验室 北京 100081%清华大学计算机科学与技术系 北京100084 北京理工大学计算机学院 北京 100081 |
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| SubjectTerms | 分层弹性运动跟踪 序贯形状聚类 贝叶斯网络 运动分段 运动连接 |
| Title | 基于分层弹性运动分析的非刚体跟踪方法 |
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