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Related Experiment Video

Updated: Oct 30, 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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Published on: May 7, 2019

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild.

Mohammad Ibrahim Sarker¹, Cristina Losada-Gutiérrez¹, Marta Marrón-Romera¹

¹Department of Electronics, Politechnics School, Campus Universitario S/N, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces a weakly supervised learning algorithm for automatic anomaly detection in surveillance videos. The method enhances reliability by reducing false negatives, improving public safety monitoring.

Keywords:

CNN RGB anomaly detection multiple instance learning video-surveillance

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Public safety relies on surveillance cameras, but manual monitoring of rare anomalies is inefficient and prone to errors.
Automatic anomaly detection in video surveillance is crucial due to the limitations of human monitoring.

Purpose of the Study:

To propose a weakly supervised learning algorithm for robust anomaly detection in video surveillance.
To improve the reliability and efficiency of anomaly detection systems.

Main Methods:

Utilizing a temporal convolutional 3D neural network (T-C3D) for spatio-temporal feature extraction.
Implementing a novel ranking loss function to differentiate anomalous from normal videos and minimize false negatives.

Main Results:

The proposed approach achieved competitive performance against state-of-the-art methods without requiring fine-tuning.
Demonstrated strong generalization capability and high sensitivity in detecting anomalies in real-world scenarios.

Conclusions:

The weakly supervised learning algorithm offers a reliable and effective solution for automatic anomaly detection in video surveillance.
The method shows significant potential for enhancing public safety through improved monitoring systems.