Tube Arrangement Using a Mesh-Based Sorting Approach in Video Synopsis

As the amount of surveillance video keeps growing rapidly, monitoring it has become harder and takes more time. Video synopsis helps by making a compact version of the original video, eliminating spatial and temporal redundancies while retaining all critical activities. In contrast to conventional a...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 32; pp. 4389 - 4393
Main Authors: Vyasdev, Saha, Koustuv, Patel, Jeel, Mahapatra, Ansuman, S, Priyadharshini
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Binary trees
Computer vision
Interpolation
Measurement
object tube
Segmentation
Signal processing algorithms
Simulated annealing
Sorting
Sorting algorithms
surveillance camera
Tensors
Three-dimensional displays
Time complexity
tube rearrangement
tube sorting
Tubes
video synopsis
Videos
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary:As the amount of surveillance video keeps growing rapidly, monitoring it has become harder and takes more time. Video synopsis helps by making a compact version of the original video, eliminating spatial and temporal redundancies while retaining all critical activities. In contrast to conventional approaches that rely on traditional tube rearrangement strategies, this work proposes a novel mesh-based tube sorting algorithm within a comprehensive video synopsis pipeline. The framework includes object segmentation and tracking using the YOLO11 segmentation model, followed by tube extraction and rearrangement using custom algorithms. Furthermore, a new evaluation metric is introduced to measure tube rearrangement algorithms, with lower computational complexity than existing metrics in the domain of video synopsis. To improve accuracy, an interpolation algorithm is also proposed to reconstruct broken object tubes caused by detection or segmentation errors, resulting in a more efficient and robust video synopsis framework.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2025.3631432