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基于自适应特征选择的多尺度相关滤波跟踪

Multi-Scale Correlation Filtering Tracker Based on Adaptive Feature Selection

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摘要

近年来, 基于相关滤波的目标跟踪算法因其具有很好的跟踪精度和明显的速度优势, 引起了研究人员的极大关注。提出一种基于自适应特征选择的多尺度相关滤波跟踪算法。首先, 提取三种互补特征, 通过相关滤波响应图评估各特征的跟踪性能, 自适应选择最优特征进行位置跟踪; 其次, 预设响应图阈值作为位置相关滤波模型更新的判断条件, 优化模型更新方式; 最后, 引入尺度相关滤波跟踪器, 进一步提高了算法的尺度适应性和跟踪精度。实验部分将该算法和近年来流行的相关滤波及非相关滤波类跟踪算法进行了对比, 结果表明, 该算法在精度上优于其他算法, 同时具有53.12 frame/s的实时跟踪速度。

Abstract

Recently, the correlation filter-based trackers have aroused increasing interest because of their good performance and high efficiency. A multi-scale correlation filtering tracker based on adaptive feature selection is presented. Firstly, we extract three complementary features to learn three independent filter models. By comparing the response maps, we evaluate the tracking performance of each feature, and then adaptively select the most representative feature for tracking. Secondly, to better handle occlusions and drifts, we improve the online model update strategy by setting peak response threshold as a criterion. Furthermore, we learn a separate filter model for scale estimation. The experimental results show that the proposed tracker achieves better accuracy compared with state-of-the-art correlation filter-based trackers and other popular trackers when running at 53.12 frame/s.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391

DOI:10.3788/aos201737.0515001

所属栏目:机器视觉

基金项目:国家自然科学基金(61201365,61401200)、江苏省普通高校研究生科研创新计划(SJLX15_0138)

收稿日期:2016-10-21

修改稿日期:2017-01-02

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作者单位    点击查看

沈 秋:南京航空航天大学航天学院, 江苏 南京 210016
严小乐:南京航空航天大学航天学院, 江苏 南京 210016
刘霖枫:南京航空航天大学航天学院, 江苏 南京 210016
孔繁锵:南京航空航天大学航天学院, 江苏 南京 210016
王丹丹:南京航空航天大学航天学院, 江苏 南京 210016

联系人作者:沈秋(shenqiu@nuaa.edu.cn)

备注:沈 秋(1982—),女,博士,讲师,主要从事视频压缩与视频处理方面的研究.

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