空军工程大学学报（自然科学版）Issue(5)：71-75,5.DOI:10.3969/j.issn.1009-3516.2014.05.018
基于压缩感知的在线多示例学习目标追踪
Visual Tracking with Multiple Instance Learning Based on Compressive Sensing
摘要
Abstract
Visual tracking is one of the most popular research topics in the domain of computer vision.It is a challenging task to develop an effective and efficient tracking algorithm because of template drift prob-lems.To alleviate the drift,the multiple instance learning (MIL)method has been applied to target track-ing.However,there must be a sufficient amount of useful data for online MIL to learn at the outset, which actually increases the computational complexity.In this paper,an effective tracking algorithm is proposed which uses an online MIL based on the compressed appearance model to accomplish obj ect track-ing.In order to decrease the computational complexity and obtain sufficient data for online learning adap-tive appearance model,Features are extracted by non-adaptive random proj ections of the multi-scale image feature space based on compressive sensing theories.The experimental results on various videos show that the proposed method has a satisfactory performance in real-time obj ect tracking.关键词
目标追踪/多示例学习/压缩感知Key words
visual tracking/multiple instance learning/compressive sensing分类
电子信息工程引用本文复制引用
韩亚颖,王元全..基于压缩感知的在线多示例学习目标追踪[J].空军工程大学学报（自然科学版）,2014,(5):71-75,5.基金项目
天津市自然科学基金资助项目(11JCZDJC15600) （11JCZDJC15600）
