压缩传感目标跟踪在多实例中的应用  

Application of Compressive Sense Target Tracking in Multiple Instance

作　　者：陈茜[1] 狄岚[1] 梁久祯[2] Chen Xi;Di Lan;Liang Jiuzhen(School of Digital Media,Jiangnan University,Wuxi,214122,China;School of Internet of Things Engineering,Jiangnan University,Wuxi,214122,China)

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]江南大学物联网工程学院,无锡214122

出　　处：《数据采集与处理》2019年第3期462-471,共10页Journal of Data Acquisition and Processing

基　　金：江苏省六大人才高峰项目(DZXX-028)资助项目;江南大学教师卓越工程资助项目

摘　　要：研究了一种基于压缩传感的实时目标跟踪算法。该算法结合多特征和压缩传感目标跟踪,增加随机测量矩阵提取多个特征用于检测,在跟踪时采用基于boosting的框架,利用多实例的正负样本包特性,提高置信区间估计,实现了实时的目标跟踪。实验结果及分析表明,本文方法在目标运动、姿态变化以及被部分遮挡的情况下,可在原压缩传感目标跟踪算法的基础上提高跟踪的可靠性;与传统的单一特征目标跟踪算法相比,由本方法提取的两种不同类型的特征具有互补性,使得跟踪的鲁棒性较好,能达到稳定、实时的跟踪效果。A real-time target tracking method based on compressive sense is investigated.Combining the multiply-features and compressive sense target tracking together,the proposed method introduces a random measurement matrix for detecting in features extraction.Based on the boosting-based frame in tracking,the accuracy of confidence interval estimation is improved by using the identities both of the positive and negative bags of multiply-features.With the proposed method,the chosen target can be tracked on line.Experimental results show that this method can achieve higher efficiency and accuracy in cases such as object moving,pose changing and occlusion.Compared with traditional single feature based target tracking methods,the complementarity of two different features extracted from the proposed method can make the tracking process more robust with a stable and real-time performance.

关 键 词：目标跟踪 压缩传感 多样本 实时跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]