基于分层弹性运动分析的非刚体跟踪方法

吕峰; 邸慧军; 陆耀; 徐光祐

doi:10.16383/j.aas.2015.c140375

基于分层弹性运动分析的非刚体跟踪方法

doi: 10.16383/j.aas.2015.c140375

吕峰^1,2,
邸慧军^1,2,
陆耀^1,2, ,,
徐光祐³

1.
北京理工大学计算机学院北京 100081;
2.
智能信息技术北京市重点实验室北京 100081;
3.
清华大学计算机科学与技术系北京 100084

基金项目:

国家自然科学基金(61273273,61003098),高等学校博士学科点专项科研基金(2012110110034),北京市教育委员会共建项目资助

详细信息

作者简介:
吕峰北京理工大学计算机学院博士研究生. 主要研究方向为目标跟踪与动作识别. E-mail: lvfeng@bit.edu.cn

通讯作者:
陆耀北京理工大学计算机学院教授.主要研究方向为神经网络, 图像和信号处理, 模式识别. 本文通信作者.E-mail: vis_yl@bit.edu.cn

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出版历程
- 收稿日期: 2014-06-25
- 修回日期: 2014-10-13
- 刊出日期: 2015-02-20

Non-rigid Tracking Method Based on Layered Elastic Motion Analysis

LV Feng^1,2,
DI Hui-Jun^1,2,
LU Yao^{1,2
, ,},
XU Guang-You³

1.
School of Computer Science, Beijing Institute of Technology, Beijing 100081;
2.
Beijing Laboratory of Intelligent Information Technology, Beijing 100081;
3.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084

Funds:

Supported by National Natural Science Foundation of China (61273273, 61003098), Research Fund for the Doctoral Program of Higher Education of China (2012110110034), and Specialized Fund for Joint Building Project of Beijing Municipal Education Commission

摘要

摘要: 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.
- 分层弹性运动跟踪 /
- 序贯形状聚类 /
- 运动分段 /
- 运动连接 /
- 贝叶斯网络
Abstract: 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.
- Layered elastic motion tracking /
- sequential shape clustering /
- motion segmentation /
- motion connection /
- Bayesian network

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