金晓康,
吴宏林,
伍悠
1.长沙理工大学综合交通运输大数据智能处理湖南省重点实验室 ??长沙 ??410114
2.长沙理工大学计算机与通信工程学院 ??长沙 ??410114
基金项目:国家自然科学基金(61402053, 61772454, 61811530332)，湖南省教育厅科学研究重点项目(16A008)
详细信息
作者简介:张建明：男，1976年生，副教授，博士，研究方向为计算机视觉、智能交通系统
金晓康：男，1993年生，硕士生，研究方向为计算机视觉、深度学习
吴宏林：男，1982年生，讲师，博士，研究方向为压缩感知
伍悠：女，1995年生，硕士生，研究方向为计算机视觉、深度学习
通讯作者:张建明 jmzhang@csust.edu.cn
中图分类号:TP391计量
文章访问数:1169
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被引次数:0
出版历程
收稿日期:2017-11-30
修回日期:2018-07-16
网络出版日期:2018-07-25
刊出日期:2018-10-01
Multi-model Real-time Compressive Tracking
Jianming ZHANG,,Xiaokang JIN,
Honglin WU,
You WU
1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
Funds:The National Natural Science Foundation of China (61402053, 61772454, 61811530332), The Scientific Research Fund of Hunan Provincial Education Department (16A008)
摘要
摘要:目标跟踪易受光照、遮挡、尺度、背景及快速运动等因素的影响，还要求较高的实时性。目标跟踪中基于压缩感知的跟踪算法实时性好，但目标外观变化较大时跟踪效果不理想。该文基于压缩感知的框架提出多模型的实时压缩跟踪算法(MMCT)，采用压缩感知来降低跟踪过程产生的高维特征，保证实时性能；通过判断前两帧的分类器最大分类分数的差值来选择最合适的模型，提高了定位的准确性；提出新的模型更新策略，按照决策分类器的不同采用固定或动态的学习率，提高了分类精度。MMCT引入的多模型没有增加计算负担，表现出优异的实时性能。实验结果表明，MMCT算法能够很好地适应光照、遮挡、复杂背景及平面旋转的情况。
关键词:目标跟踪/
压缩感知/
实时/
多模型/
动态学习率
Abstract:Object tracking is easily influenced by illumination, occlusion, scale, background clutter, and fast motion, and it requires higher real-time performance. The object tracking algorithm based on compressive sensing has a better real-time performance but performs weakly in tracking when object appearance is changed greatly. Based on the framework of compressive sensing, a Multi-Model real-time Compressive Tracking (MMCT) algorithm is proposed, which adopts the compressive sensing to decrease the high dimensional features for the tracking process and to satisfy the real-time performance. The MMCT algorithm selects the most suitable classifier by judging the maximum classification score difference of classifiers in the previous two frames, and enhances the accuracy of location. The MMCT algorithm also presents a new model update strategy, which employs the fixed or dynamic learning rates according to the differences of decision classifiers and improves the precision of classification. The multi-model introduced by MMCT does not increase the computational burden and shows an excellent real-time performance. The experimental results indicate that the MMCT algorithm can well adapt to illumination, occlusion, background clutter and plane-rotation.
Key words:Object tracking/
Compressive Sensing (CS)/
Real-time/
Multi-model/
Dynamic learning rate
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https://jeit.ac.cn/article/exportPdf?id=b7c182bb-9acf-4106-a3ff-6c2dd1288190
