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

  • 1212: Deep Learning Techniques for Infrared Image/Video Understanding
  • Published:
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Abstract

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.

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Authors and Affiliations

  1. Research Scholar, Indian Institute of Technology (ISM.), Dhanbad, Jharkhand, India

    Mritunjay Rai

  2. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, NCR Campus, Ghaziabad, India

    Rohit Sharma

  3. KIIT, Bhubaneshwar, Orissa, India

    Suresh Chandra Satapathy

  4. Department of CSE, Galgotias University, Uttar Pradesh, Gautam Budha Nagar, India

    Dileep Kumar Yadav

  5. Department of MME, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

    Tanmoy Maity

  6. Department of ECE, RKGIT, Ghaziabad, India

    R. K. Yadav

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  1. Mritunjay Rai
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  2. Rohit Sharma
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  3. Suresh Chandra Satapathy
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  4. Dileep Kumar Yadav
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  5. Tanmoy Maity
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  6. R. K. Yadav
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Correspondence to Rohit Sharma.

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Rai, M., Sharma, R., Satapathy, S.C. et al. An improved statistical approach for moving object detection in thermal video frames. Multimed Tools Appl 81, 9289–9311 (2022). https://doi.org/10.1007/s11042-021-11548-x

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  • DOI: https://doi.org/10.1007/s11042-021-11548-x

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