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Related Experiment Video

Updated: Nov 18, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

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Abnormal Event Detection and Localization via Adversarial Event Prediction.

Jongmin Yu, Younkwan Lee, Kin Choong Yow

    IEEE Transactions on Neural Networks and Learning Systems
    |February 3, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Adversarial Event Prediction (AEP) detects abnormal events by learning normal event correlations. This novel approach uses adversarial learning to improve prediction accuracy without needing explicit anomaly data.

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    Last Updated: Nov 18, 2025

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Event detection often requires explicit anomaly samples or complementary data.
    • Predicting future events from past data is crucial for anomaly detection.

    Purpose of the Study:

    • To introduce Adversarial Event Prediction (AEP), a novel method for abnormal event detection.
    • To develop a prediction model that identifies correlations between present and future events.
    • To enhance anomaly detection by restricting representation learning for past events.

    Main Methods:

    • AEP utilizes an event prediction framework trained on normal event samples.
    • Adversarial learning is proposed to distinguish between past and future event representations.
    • The method focuses on learning representations for predicting future events while constraining past event learning.

    Main Results:

    • AEP effectively detects anomalies without requiring optical flow or explicit abnormal event samples.
    • The proposed adversarial learning enhances the model's ability to learn normal event representations.
    • Experiments on benchmark datasets (UCSD-Ped, CUHK Avenue, Subway, UCF-Crime) show superior performance compared to state-of-the-art methods.

    Conclusions:

    • Adversarial learning significantly improves the derivation of normal event models in AEP.
    • AEP trained with adversarial learning surpasses existing state-of-the-art methods in abnormal event detection.
    • The approach offers an efficient and effective solution for anomaly detection in event sequences.