目标实体轮廓跟踪中动态高阶能量最小化问题的研究

项目名称： 目标实体轮廓跟踪中动态高阶能量最小化问题的研究

项目编号： No.61305012

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 宋华军

作者单位： 中国石油大学（华东）

项目金额： 24万元

中文摘要： 视频中的目标实体轮廓跟踪通常抽象为能量最小化问题。高维图割算法是计算高阶能量函数最小化的最有效方法之一。然而，现存高维图割方法存在以下局限：1）特定形式的高阶项无法刻画目标复杂视觉信息；2）独立帧处理方式导致目标跟踪运算效率低。针对上述缺陷，本课题研究广义高阶能量函数的规整化高维s/t图模型表达方法，克服特定高阶项的局限性，丰富高维图割刻画复杂目标和背景的能力。同时，根据视频中目标和背景的连贯性，研究连续帧中高阶能量的增量动态变化规律，设计高阶项参数转移方法，实现高维图割的动态运算，提升目标实体轮廓跟踪的计算效率。综上，本课题将设计新的动态高维图割框架，有机地拓宽图割类方法的理论外延，有效地解决目标实体轮廓跟踪中高阶能量的广义建模和高效运算的问题。

中文关键词： 目标跟踪；动态图割；3D图像配准；溢油检测；

英文摘要： Silhouette tracking is usually formulated as an energy minimization problem. Higher-order graph-cut is one of the most effective methods for minimizing the higher-order energy functions. However, in existing higher-order graph-cut methods there are certain limitations as follows: 1) they tend to be suited to specific forms of higher order energy functions and cannot charaterize complex visual information of objects, and 2) they are based on a single frame image processing strategy and are thus computationally inefficient. To address these shortcomings, we investigate how to represent a generalized higher order energy function using a uniform high-order s/t graph model, overcoming the limitation of the specific higher-order terms in the energy function and enriching the ability of graph-cut methods for characterizing complex targets and backgrounds. Furthermore, according to the coherence of targets and backgrounds in the video frame sequence, we develop a parameter transfer method for incrementally characterizing the changes between higher-order graphs for consecutive frames. Our method dynamically implements higher-order graph-cut algorithms and enables an efficient computation for high-order graph-cut in silhouette tracking. In summary, we will develop a novel framework based on dynamic higher-order graph-cut

英文关键词： target tracking；dynamic Gragh-cut；3D image registration；oil spill detection；