在线学习机制下的Snake轮廓跟踪  

Snake Contour Tracking Under Online Learning Mechanism

作　　者：沈宋衍 陈莹 

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《计算机工程》2015年第4期195-198,共4页Computer Engineering

基　　金：国家自然科学基金资助项目(61104213);江苏省自然科学基金资助项目(BK2011146)

摘　　要：针对复杂环境下非刚体目标轮廓跟踪存在跟踪失败的问题,提出一种基于在线学习的Snake模型及其轮廓跟踪算法。利用跟踪-学习-检测(TLD)机制实现目标快速跟踪,通过跟踪结果在线更新Snake模型约束,进而提高目标轮廓跟踪的准确性。初始化阶段,在Grab Cut算法的基础上,将待跟踪目标分成若干个子块,并在后续跟踪过程中,利用TLD实现各子目标的定位跟踪,形成目标的轮廓置信图。同时针对各子目标提取特征,产生正负样本,更新各子目标跟踪模型。应用置信图建立参数化Snake模型的约束条件,进而得到目标轮廓。实验结果表明,该算法能适应光暗变化与较为复杂坏境下的跟踪,并获得精确的轮廓。For non-rigid target contour tracking in a complicated environment has tracking failure problems,this paper proposes a snake model and its contour tracking algorithm based on online learning. The algorithm utilizes the Tracking-Learning-Detection（ TLD） mechanism to achieve the goal of fast tracking,and updates snake model constraints through the tracking results to improve the accuracy of the target contour tracking. In the phase of initialization,the target to be tracking is divided into several blocks on the basis of Grab Cut algorithm,and the algorithm realizes the sub-targets locating and tracking by the use of TLD in the subsequent tracking process,w hich forms the confident map of target outline. At the same time,the algorithm produces positive and negative samples and updates each target tracking model for each target feature extraction. The constraint of parameterized snake model is built through confident map and the contour of target is obtained. Experimental results show that the algorithm can adapt to the changing light and dark,and even more complex tracking environment,and obtains precise contour.

关 键 词：轮廓跟踪 GrabCut算法 SNAKE模型 跟踪-学习-检测算法 在线学习 置信图 

分 类 号：TP37[自动化与计算机技术—计算机系统结构]