蒲磊^1,,,
冯新喜²,
侯志强³,
余旺盛²
1.空军工程大学研究生院 ??西安 ??710077
2.空军工程大学信息与导航学院 ??西安 ??710077
3.西安邮电大学计算机学院 ??西安 ??710121
基金项目:国家自然科学基金(61571458, 61473309, 41601436)

详细信息

作者简介:蒲磊：男，1991年生，博士生，研究方向为计算机视觉、目标跟踪
冯新喜：男，1964年生，教授，研究方向为信息融合、模式识别
侯志强：男，1973年生，教授，研究方向为图像处理、计算机视觉
余旺盛：男，1985年生，讲师，研究方向为图像处理、模式识别

通讯作者:蒲磊　warmstoner@163.com

中图分类号:TP391.4

计量

文章访问数:1617
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被引次数:0

出版历程

收稿日期:2018-08-07
修回日期:2019-01-21
网络出版日期:2019-02-15
刊出日期:2019-07-01

Robust Visual Tracking Based on Spatial Reliability Constraint

Lei PU^1,,,
Xinxi FENG²,
Zhiqiang HOU³,
Wangsheng YU²
1. Graduate College, Air Force Engineering University, Xi’an 710077, China
2. Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
3. School of Computer Science and Technology, Xian University of Posts and Telecommunications, Xi’an 710121, China
Funds:The National Natural Science Foundation of China (61571458, 61473309, 41601436)


摘要
摘要:针对复杂背景下目标容易发生漂移的问题，该文提出一种基于空间可靠性约束的目标跟踪算法。首先通过预训练卷积神经网络(CNN)模型提取目标的多层深度特征，并在各层上分别训练相关滤波器，然后对得到的响应图进行加权融合。接着通过高层特征图提取目标的可靠性区域信息，得到一个二值注意力矩阵，最后将得到的二值矩阵用于约束融合后响应图的搜索范围，范围内的最大响应值即为目标的中心位置。为了处理长时遮挡问题，该文提出一种基于首帧模板信息的随机选择更新策略。实验结果表明，该算法在应对相似背景干扰、遮挡、超出视野等多种场景均有良好的性能表现。
关键词:视觉跟踪/
空间可靠性约束/
深度特征/
相关滤波/
模型更新
Abstract:Because of the problem that the target is prone to drift in complex background, a robust tracking algorithm based on spatial reliability constraint is proposed. Firstly, the pre-trained Convolutional Neural Network (CNN) model is used to extract the multi-layer deep features of the target, and the correlation filters are respectively trained on each layer to perform weighted fusion of the obtained response maps. Then, the reliability region information of the target is extracted through the high-level feature map, a binary matrix is obtained. Finally, the obtained binary matrix is used to constrain the search area of the response map, and the maximum response value in the area is the target position. In addition, in order to deal with the long-term occlusion problem, a random selection model update strategy with the first frame template information is proposed. The experimental results show that the proposed algorithm has good performance in dealing with similar background interference, occlusion, and other scenes.
Key words:Visual tracking/
Spatial reliability constraint/
Deep features/
Correlation filter/
Model update



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