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。
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.
作者
蒲磊
冯新喜
侯志强
余旺盛
PU Lei;FENG Xinxi;HOU Zhiqiang;YU Wangsheng(Graduate College,Air Force Engineering University,Xi'an 710077,China;Institute of Information and Navigation,Air Force Engineering University,Xi'an 710077,China;School of Computer Science and Technology,Xian University of Posts and Telecommunications,Xi'an 710121,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2019年第7期1650-1657,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61571458,61473309,41601436)~~
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
Visual tracking
Spatial reliability constraint
Deep features
Correlation filter
Model update