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Object detection driven composite block motion estimation algorithm for surveillance video coding.

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Title: Object detection driven composite block motion estimation algorithm for surveillance video coding.
Authors: Kumar Pal, Arup1 (AUTHOR) arupkrpal@iitism.ac.in, Biswas, Bhaskar1 (AUTHOR), Digamber Jichkar, Mihir1 (AUTHOR), Nandan Jena, Adarsh1 (AUTHOR), Kumar, Manish1 (AUTHOR)
Source: Multimedia Tools & Applications. Sep2025, Vol. 84 Issue 32, p39719-39746. 28p.
Subjects: Motion estimation (Signal processing), Video compression, Deep learning, Object recognition (Computer vision), Motion analysis, Video coding, Computer vision, Video surveillance
Abstract: The accurate estimation of motion in video sequences is a critical task in various computer vision applications, such as video compression, video surveillance, and autonomous navigation. Block matching is a popular motion estimation method through which we find the best match between blocks of pixels in consecutive frames. However, using a single block matching algorithm for the entire frame may be inefficient, especially when dealing with dynamic scenes containing moving objects. In this paper, we propose a novel approach for motion estimation using composite block-matching algorithms based on object detection. Our method first detects the moving objects in the video frame using a deep learning-based object detection algorithm. Then, we use one block-matching algorithm for the area of the frame where the objects are detected and another block-matching algorithm for the remaining part of the frame. The rationale behind using two different algorithms is that the area containing the objects has more complex motion patterns and may require a more sophisticated and rigorous algorithm than the rest of the frame. Our results show that the proposed method achieves higher accuracy and faster processing time compared to any single motion estimation method. The proposed composite block matching algorithm provides more accurate motion estimation in the object regions, leading to better object tracking and fewer motion artefacts. Moreover, using a simpler block-matching algorithm for the non-object regions reduces computational overhead, making the overall process more efficient for compressing surveillance video. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Object detection driven composite block motion estimation algorithm for surveillance video coding.
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  Data: <searchLink fieldCode="DE" term="%22Motion+estimation+%28Signal+processing%29%22">Motion estimation (Signal processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Video+compression%22">Video compression</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+analysis%22">Motion analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Video+coding%22">Video coding</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Video+surveillance%22">Video surveillance</searchLink>
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  Data: The accurate estimation of motion in video sequences is a critical task in various computer vision applications, such as video compression, video surveillance, and autonomous navigation. Block matching is a popular motion estimation method through which we find the best match between blocks of pixels in consecutive frames. However, using a single block matching algorithm for the entire frame may be inefficient, especially when dealing with dynamic scenes containing moving objects. In this paper, we propose a novel approach for motion estimation using composite block-matching algorithms based on object detection. Our method first detects the moving objects in the video frame using a deep learning-based object detection algorithm. Then, we use one block-matching algorithm for the area of the frame where the objects are detected and another block-matching algorithm for the remaining part of the frame. The rationale behind using two different algorithms is that the area containing the objects has more complex motion patterns and may require a more sophisticated and rigorous algorithm than the rest of the frame. Our results show that the proposed method achieves higher accuracy and faster processing time compared to any single motion estimation method. The proposed composite block matching algorithm provides more accurate motion estimation in the object regions, leading to better object tracking and fewer motion artefacts. Moreover, using a simpler block-matching algorithm for the non-object regions reduces computational overhead, making the overall process more efficient for compressing surveillance video. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-025-20722-4
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        Text: English
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        Type: general
      – SubjectFull: Video compression
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      – SubjectFull: Deep learning
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      – SubjectFull: Object recognition (Computer vision)
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      – SubjectFull: Motion analysis
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      – SubjectFull: Video coding
        Type: general
      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Video surveillance
        Type: general
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      – TitleFull: Object detection driven composite block motion estimation algorithm for surveillance video coding.
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              M: 09
              Text: Sep2025
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              Y: 2025
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