Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes Article Swipe

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Z. Jane Wang , Fouzi Harrou , Ying Sun , Marc G. Genton ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.32388/0n1ebc

Detecting anomalies in crowded video scenes is critical for public safety, enabling timely identification of potential threats. This study explores video anomaly detection within a Functional Data Analysis framework, focusing on the application of the Magnitude-Shape (MS) Plot. Autoencoders are used to learn and reconstruct normal behavioral patterns from anomaly-free training data, resulting in low reconstruction errors for normal frames and higher errors for frames with potential anomalies. The reconstruction error matrix for each frame is treated as multivariate functional data, with the MS-Plot applied to analyze both magnitude and shape deviations, enhancing the accuracy of anomaly detection. Using its capacity to evaluate the magnitude and shape of deviations, the MS-Plot offers a statistically principled and interpretable framework for anomaly detection. The proposed methodology is evaluated on two widely used benchmark datasets, UCSD Ped2 and CUHK Avenue, demonstrating promising performance. It performs better than traditional univariate functional detectors (e.g., FBPlot, TVDMSS, Extremal Depth, and Outliergram) and several state-of-the-art methods. These results highlight the potential of the MS-Plot-based framework for effective anomaly detection in crowded video scenes.

Related Topics

Anomaly Detection

Benchmark (Surveying)

Plot (Graphics)

Computer Science

Artificial Intelligence

Condensed Matter Physics

Astronomy

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Anomaly detection Benchmark (surveying) Plot (graphics) Computer science Artificial intelligence Magnitude (astronomy) Anomaly (physics) Pattern recognition (psychology) Frame (networking) Univariate Computer vision Multivariate statistics Mathematics Machine learning Statistics Geography Cartography Physics Telecommunications Condensed matter physics Astronomy

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Type: preprint
Language: en
Landing Page: https://doi.org/10.32388/0n1ebc
OA Status: gold
Cited By: 2
References: 89
Related Works: 10
OpenAlex ID: https://openalex.org/W4407029570

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DOI: https://doi.org/10.32388/0n1ebc

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Title: Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-01-31

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Authors: Z. Jane Wang, Fouzi Harrou, Ying Sun, Marc G. Genton

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Landing page: https://doi.org/10.32388/0n1ebc

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Concepts: Anomaly detection, Benchmark (surveying), Plot (graphics), Computer science, Artificial intelligence, Magnitude (astronomy), Anomaly (physics), Pattern recognition (psychology), Frame (networking), Univariate, Computer vision, Multivariate statistics, Mathematics, Machine learning, Statistics, Geography, Cartography, Physics, Telecommunications, Condensed matter physics, Astronomy

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Cited by: 2

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