An improved statistical approach for moving object detection in thermal video frames

被引：0
作者：
Mritunjay Rai
Rohit Sharma
Suresh Chandra Satapathy
Dileep Kumar Yadav
Tanmoy Maity
R. K. Yadav
机构：
[1] Research Scholar,Department of Electronics and Communication Engineering
[2] Indian Institute of Technology (ISM.),Department of CSE
[3] SRM Institute of Science and Technology,Department of MME
[4] NCR Campus,Department of ECE
[5] KIIT,undefined
[6] Galgotias University,undefined
[7] Uttar Pradesh,undefined
[8] Indian Institute of Technology (ISM),undefined
[9] RKGIT,undefined
来源：
Multimedia Tools and Applications | 2022年 / 81卷
关键词：
Thermal video surveillance; Object detection; Background modeling; Morphological tool; Gaussian mixture model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In a video surveillance system, background modeling is assumed to be a fundamental technique for moving object detection. The surveillance system based on thermal video overcomes many challenges, such as background variations, varying light intensity, external illumination source, and so on. This paper presents a new method for background modeling and background subtraction. The method utilizes the combined approach of Fisher's Linear Discriminant and Relative Entropy for pixel based classification and detection of moving objects in thermal video frames. The experimental results show the higher average value of various performance indicators like Accuracy, ROC, and F-measure. In contrast, the percentage of false classification and total error is minimum and also has lesser execution time. The method outperforms when compared with the other existing methods.
引用
收藏
页码：9289 / 9311
页数：22
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