基于不规则区域精确表征的非刚体运动目标在线跟踪研究

项目名称： 基于不规则区域精确表征的非刚体运动目标在线跟踪研究

项目编号： No.61301194

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 张蓬

作者单位： 西北工业大学

项目金额： 26万元

中文摘要： 不规则区域的精确表征是非刚体目标跟踪的主要研究难点之一。本项目针对现有非刚体跟踪中在线产生训练样本误差大，目标状态更新不鲁棒的问题，将基于规则几何区域描述的跟踪泛化为普适的不规则区域跟踪，创新性的提出了一套基于不规则区域精确表征的非刚体目标在线跟踪方法，从新的角度解决目前制约任意形状物体在线跟踪的关键问题，在统一的数学模型下实现非刚体跟踪。研究内容主要包括：(1)构造基于目标动态边缘梯度动态变化的能量变化模型，实现非刚体目标精确分割；(2)研究基于临域约束的目标子样本特征空间映射方法，实现目标子区域的精确表征；(3)研究子样本轨迹参数化的分类器误差定义方法,实现目标检测在线正负约束学习；（4）实现目标边缘平滑及跟踪性能优化并对整体方法进行性能评估。本项目的研究在智能监控、人机交互、多媒体压缩、视觉传器网络等领域有重要研究意义和广泛应用前景。

中文关键词： 非刚体；跟踪；不规则区域；表征；在线学习

英文摘要： As the spring up of motion sensing, HD video coding and visual sensor networks, their further development raises the request for the support of accurate object tracking, which is a fundamental technique of these applications. Unfortunately, realistic object tracking is not easy because of the challenges from appearance extrinsic and intrinsic variations. Especially for a non-rigid object, since its geometry features may change totally different from its initial state as tracking goes by, it is not possible for the regular shape based online tracking-by-learning approaches to achieve a long-term accurate tracking. In this project, we innovatively propose a non-rigid object online tracking based on more accurate representation of irregular areas by using pair-wise gradient flow. By dynamic gradient change modeling, subsample representation with contex constraints and error denifition of subsample moving trajectory, some significant problems in non-rigid target tracking are supposed to be resolved. This project will carry out a comprehensive investigation from the aspects containing non-rigid object segmentation modeling, feature extraction and description for training samples, classifier online learning-by-classification mechanism, target border matting and tracking performance analysis. Based on the work of thi

英文关键词： Non-rigid Target；Tracking；Irregular Areas；Representation；Online Learning