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基于在线检测和尺度自适应的相关滤波跟踪

Correlation Filter Tracking Based on Online Detection and Scale-Adaption

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摘要

针对相关滤波跟踪在遮挡及目标尺度变化等情况下容易跟踪失败的问题, 提出一种基于在线检测和尺度自适应的相关滤波跟踪算法。相关滤波跟踪器融合方向梯度直方图特征、颜色属性特征和光照不变特征进行目标定位;通过局部稀疏表示模型的重构残差进行遮挡判别, 如果发生遮挡则进行在线支持向量机检测, 实现目标重定位;进行由粗至精的尺度估计, 通过尺度预估计和牛顿迭代法得到目标的精确尺度。采用均衡的模型更新策略, 固定更新相关滤波器, 保守更新稀疏表示模型和支持向量机。实验结果表明:与现有跟踪算法相比, 所提算法能有效降低遮挡、目标尺度变化等复杂因素的干扰, 并在50组测试序列上取得较高的距离精度和成功率, 其整体性能优于其他对比算法。

Abstract

In correlation filter tracking, occlusion and object scale change can lead to tracking failure easily. To deal with this problem, a correlation filter tracking algorithm based on online detection and scale-adaption is proposed. The target is initially located through a correlation filter tracker fusing histogram features of oriented gradient, color attribute features and illumination invariant features. The reconstruction residual of local sparse representation model is used for occlusion discrimination. If occlusion occurs, online support vector machine detection will be carried out and target relocating will be realized. Scale estimation from coarse to precise is carried out, and precise scale of target is obtained by scale pre-estimation and Newton iterative method. A balanced model updating strategy is used to update correlation filter regularly and update sparse representation model and support vector machine conservatively. Experimental results show that, compared with existing tracking algorithms, the proposed algorithm can effectively reduce the occlusion, target scale change and other complicated factors, and can gain higher distance precision and success rate on 50 groups of test sequences. The overall performance of the proposed algorithm is better than other contrast algorithms.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391

DOI:10.3788/aos201838.0215002

所属栏目:机器视觉

基金项目:国家自然科学基金(61601513, 61379151)、河南省杰出青年基金(144100510001)

收稿日期:2017-08-28

修改稿日期:2017-09-29

网络出版日期:--

作者单位    点击查看

王艳川:国家数字交换系统工程技术研究中心, 河南 郑州 450000
黄海:国家数字交换系统工程技术研究中心, 河南 郑州 450000
李邵梅:国家数字交换系统工程技术研究中心, 河南 郑州 450000
高超:国家数字交换系统工程技术研究中心, 河南 郑州 450000

联系人作者:王艳川(87-chuan@163.com)

备注:王艳川(1987-), 男, 硕士研究生, 主要从事视觉目标跟踪方面的研究。E-mail: 87-chuan@163.com

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引用该论文

Wang Yanchuan,Huang Hai,Li Shaomei,Gao Chao. Correlation Filter Tracking Based on Online Detection and Scale-Adaption[J]. Acta Optica Sinica, 2018, 38(2): 0215002

王艳川,黄海,李邵梅,高超. 基于在线检测和尺度自适应的相关滤波跟踪[J]. 光学学报, 2018, 38(2): 0215002

被引情况

【1】熊昌镇,车满强,王润玲,卢颜. 稳健的双模型自适应切换实时跟踪算法. 光学学报, 2018, 38(10): 1015002--1

【2】茅正冲,陈海东. 基于核相关滤波的长期目标跟踪算法. 激光与光电子学进展, 2019, 56(1): 10702--1

【3】尹明锋,薄煜明,朱建良,吴盘龙. 基于通道可靠性的多尺度背景感知相关滤波跟踪算法. 光学学报, 2019, 39(5): 515002--1

【4】何 冉,陈自力,刘建军,高喜俊. 自适应上下文感知相关滤波目标跟踪. 电光与控制, 2019, 26(5): 59-63

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