改进的核相关滤波器目标跟踪算法

doi:10.11772/j.issn.1001-9081.2015.12.3550

计算机应用 ›› 2015, Vol. 35 ›› Issue (12): 3550-3554.DOI: 10.11772/j.issn.1001-9081.2015.12.3550

改进的核相关滤波器目标跟踪算法

余礼杨, 范春晓, 明悦

北京邮电大学电子工程学院, 北京 100876

收稿日期:2015-05-28 修回日期:2015-08-21 出版日期:2015-12-10 发布日期:2015-12-10
通讯作者: 余礼杨(1991-),男,浙江苍南人,硕士研究生,主要研究方向:图像处理、目标检测、目标跟踪
作者简介:范春晓(1962-),女,辽宁抚顺人,教授,博士生导师,主要研究方向:图像处理、数据挖掘;明悦(1984-),女,北京人,讲师,博士,主要研究方向:人脸识别、三维重建。
基金资助:
国家自然科学基金资助项目(61402046)。

Improved target tracking algorithm based on kernelized correlation filter

YU Liyang, FAN Chunxiao, MING Yue

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2015-05-28 Revised:2015-08-21 Online:2015-12-10 Published:2015-12-10

摘要/Abstract

摘要： 针对传统单目标的核相关滤波器(KCF)跟踪算法在目标尺度变化的跟踪中存在的问题,提出了一种基于相关滤波器(CF)和尺度金字塔的多尺度核相关滤波器(SKCF)跟踪算法。首先通过传统KCF跟踪算法中分类器的响应计算当前目标是否受到遮挡,在未受到遮挡的情况下,对当前目标建立尺度金字塔;然后通过相关滤波器求取尺度金字塔的最大响应得到当前目标尺度信息;最后使用新目标图像为训练样本更新目标的外观模型和尺度模型。与核化的结构化输出(Struck)算法、KCF算法、跟踪-学习-检测(TLD)算法和多示例学习(MIL)算法进行对比,实验结果表明,所提出的多尺度核相关滤波器(SKCF)跟踪算法在五种算法中精确度和重合度都取到最高值。所提算法能够广泛应用于目标跟踪领域,对目标进行准确的跟踪。

关键词: 目标跟踪, 多尺度, 相关滤波器, 判别模型, 遮挡检测

Abstract: Focusing on the issue that the Kernelized Correlation Filter (KCF) tracking algorithm has poor performance in handling scale-variant target, a multi-scale tracking algorithm called Scale-KCF (SKCF) based on Correlation Filter (CF) and multi-scale image pyramid was proposed. Firstly, the occlusion status of the target was got through the response of the conventional KCF algorithm's classifier. The multi-scale image pyramid was built for the occluded target. Secondly, the scale information of the target was obtained by calculating the correlation filter's maximum response on the multi-scale image pyramid. Finally, the appearance model and the scale model of the target were updated with the fresh target. The experimental results on comparison with some state-of-the-art trackers such as Structured Output tracking with kernel (Struck), KCF, Tracking-Learning-Detection (TLD) and Multiple Instance Learning (MIL) demonstrate that the proposed tracker of SKCF achieves the best accuracy and overlap rate than other algorithms. Meanwhile, the proposed tracker can be widely used in target tracking and achieve high precise target tracking.

Key words: target tracking, multi-scale, Correlation Filter (CF), discriminative model, occlusion detection

中图分类号:

TP391.4

余礼杨, 范春晓, 明悦. 改进的核相关滤波器目标跟踪算法[J]. 计算机应用, 2015, 35(12): 3550-3554.

YU Liyang, FAN Chunxiao, MING Yue. Improved target tracking algorithm based on kernelized correlation filter[J]. Journal of Computer Applications, 2015, 35(12): 3550-3554.

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改进的核相关滤波器目标跟踪算法

Improved target tracking algorithm based on kernelized correlation filter

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