王鹏^{1, 2, 3},
孙梦宇^2,,,
王海燕⁴,
李晓艳²,
吕志刚²
1.西北工业大学航海学院 西安 710072
2.西安工业大学电子信息工程学院 西安 710021
3.海洋声学信息感知工业和信息化部重点实验室(西北工业大学) 西安 710072
4.陕西科技大学电子信息与人工智能学院 西安 710021
基金项目:国家自然科学基金(61271362)，国家重点研发计划(2016YFC1400200)，陕西省科技厅重点研发计划(2019GY-022、2019GY-066), 2019年西安市未央区科技计划项目(201923)

详细信息

作者简介:王鹏：男，1978年生，教授，研究方向为机器视觉、模式识别、图像处理
孙梦宇：男，1993年生，硕士生，研究方向为目标跟踪
王海燕：男，1965年生，教授，研究方向为现代信号检测与现代信息处理
李晓艳：女，1982年生，讲师，研究方向为目标检测、目标识别
吕志刚：男，1978年生，副教授，研究方向为模式识别

通讯作者:孙梦宇　1215200684@qq.com

中图分类号:TN911.73; TP391

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被引次数:0

出版历程

收稿日期:2019-07-29
修回日期:2020-03-25
网络出版日期:2020-04-03
刊出日期:2020-08-18

An Object Tracking Algorithm with Channel Reliability and Target Response Adaptation

Peng WANG^{1, 2, 3},
Mengyu SUN^2,,,
Haiyan WANG⁴,
Xiaoyan LI²,
Zhigang Lü²
1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2. School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China
3. Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, China
4. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China
Funds:The National Natural Science Foundation of China (61271362),The National Key Research and Development Project (2016YFC1400200), The Key Science and Technology Program of Shaanxi Province (2019GY-022, 2019GY-066), Weiyang District of Xi’an 2019 Science and Technology Program (201923)


摘要
摘要:为解决基于时空正则项的目标跟踪算法(STRCF)在目标短时遮挡时定位精度低和目标旋转时尺度估计不准确的问题，该文提出了一种目标响应自适应的通道可靠性跟踪算法。该算法在目标模型训练时加入了目标响应正则项，通过在求解过程中更新理想目标响应函数，使得目标被短时遮挡后可重新跟踪目标；加入通道可靠性评价各特征通道的可靠性，提高了模型对目标的表达能力；将目标图像转换至对数极坐标系下训练尺度滤波器，提高在目标旋转时的尺度估计精度。实验结果表明，该文所提算法较STRCF在平均中心位置误差中降低了28.54个像素，在平均重叠率中提高了22.8%，在OTB2015数据集下成功率曲线下面积较STRCF提高了1.5%。
关键词:目标跟踪/
相关滤波/
目标响应自适应/
通道可靠性/
尺度滤波
Abstract:In order to solve the problems of lower precision of target location in short-term occlusion and inaccurate of scale estimation of target in rotation by Spatial-Temporal Regularized Correlation Filters (STRCF), an object tracking algorithm with channel reliability and target response adaptation is proposed in this paper. In this algorithm, target response regularization is added to train target model. By updating the ideal target response function in the process of solving model, the target can be tracked again after being occluded for a short time. The reliability of each feature channel is evaluated by coefficient of channel reliability, which can improves the model's expression of the target. Scale filters can be trained in log-polar coordinates to improve the accuracy of scale estimation when target is rotating. The experimental results show that the proposed algorithm reduces 28.54 pixels in the average center position error and improves the average overlap rate by 22.8% compared with STRCF.
Key words:Object tracking/
Correlation filter/
Target response adaptation/
Channel reliability/
Scale correlation filter



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