滤波器(信号处理)
集合卡尔曼滤波器
人工智能
扩展卡尔曼滤波器
算法
自适应滤波器
噪音(视频)
数学
模式识别(心理学)
协方差
正规化(语言学)
不变扩展卡尔曼滤波器
快速卡尔曼滤波
核自适应滤波器
作者
Sheng Feng,Keli Hu,En Fan,Zhao Liping,Chengdong Wu
出处
期刊:IEEE Transactions on Image Processing
日期:2021-02-24
卷期号:30: 3263-3278
被引量:1
标识
DOI:10.1109/tip.2021.3060164
摘要
We consider visual tracking in numerous applications of computer vision and seek to achieve optimal tracking accuracy and robustness based on various evaluation criteria for applications in intelligent monitoring during disaster recovery activities. We propose a novel framework to integrate a Kalman filter (KF) with spatial-temporal regularized correlation filters (STRCF) for visual tracking to overcome the instability problem due to large-scale application variation. To solve the problem of target loss caused by sudden acceleration and steering, we present a stride length control method to limit the maximum amplitude of the output state of the framework, which provides a reasonable constraint based on the laws of motion of objects in real-world scenarios. Moreover, we analyze the attributes influencing the performance of the proposed framework in large-scale experiments. The experimental results illustrate that the proposed framework outperforms STRCF on OTB-2013, OTB-2015 and Temple-Color datasets for some specific attributes and achieves optimal visual tracking for computer vision. Compared with STRCF, our framework achieves AUC gains of 2.8%, 2%, 1.8%, 1.3%, and 2.4% for the background clutter, illumination variation, occlusion, out-of-plane rotation, and out-of-view attributes on the OTB-2015 datasets, respectively. For sporting events, our framework presents much better performance and greater robustness than its competitors.
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