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Related Experiment Video

Updated: Nov 8, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.3K

Localizing Anomalies From Weakly-Labeled Videos.

Hui Lv, Chuanwei Zhou, Zhen Cui

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 19, 2021
    PubMed
    Summary

    This study introduces a Weakly Supervised Anomaly Localization (WSAL) method to precisely pinpoint anomalous events in videos. The approach effectively uses temporal and spatial context for accurate anomaly detection and localization.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Video anomaly detection is challenging, especially in precisely localizing events temporally.
    • Existing methods often struggle with accurate temporal localization of anomalies within video sequences.

    Purpose of the Study:

    • To propose a Weakly Supervised Anomaly Localization (WSAL) method for accurate temporal localization of anomalous segments.
    • To develop a model that effectively utilizes both temporal and spatial context for anomaly detection.

    Main Methods:

    • A high-order context encoding model is proposed to extract semantic representations and measure dynamic variations.
    • Spatial context information is utilized by deriving immediate semantics from segment representations.
    • An enhancement strategy is introduced to mitigate noise and lack of localization guidance.

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    Last Updated: Nov 8, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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    Main Results:

    • The proposed WSAL method achieves state-of-the-art performance on the UCF-Crime and TAD datasets.
    • Experiments demonstrate the effectiveness of the model's components in anomaly localization.
    • A new traffic anomaly dataset (TAD) was created to enhance benchmark diversity.

    Conclusions:

    • The WSAL method offers a significant advancement in temporally localizing anomalies in videos.
    • The approach effectively integrates semantic and dynamic information for robust anomaly detection.
    • The introduction of the TAD dataset contributes to more diverse and challenging anomaly detection benchmarks.