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Related Experiment Video

Updated: Jul 1, 2026

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

Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.

Pinnapureddy Manasa1, Maragoni Mahendar1, Purude Vaishali Narayanrao1

  • 1Department of Computer Science and Engineering, Neil Gogte Institute of Technology, Hyderabad, India.

Big Data
|June 30, 2026
PubMed
Summary

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This study introduces an artificial intelligence (AI) system for automated surveillance. The AI system uses deep learning models to detect anomalous activities in video footage, enhancing security efficiency and accuracy.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional manual surveillance is inefficient and prone to human error, especially in high-stakes environments.
  • The increasing need for robust security in sensitive organizations necessitates advanced monitoring solutions.
  • Current security systems struggle to cope with the volume of video data, risking missed critical events.

Purpose of the Study:

  • To develop an intelligent artificial intelligence (AI)-powered system for automated detection of anomalous activities in surveillance footage.
  • To enhance the efficiency and accuracy of security monitoring in confidential and highly sensitive organizations.
  • To provide a proactive and seamless monitoring solution that overcomes human limitations in traditional surveillance.

Main Methods:

Keywords:
anomaly detectionartificial intelligencebig datamasked autoencodersurveillance systemvideo analysis

Related Experiment Videos

Last Updated: Jul 1, 2026

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

  • Utilized a deep learning framework, specifically the Video Masked Autoencoder model, for video understanding.
  • Employed transformer models, including Video Vision Transformers, for effortless detection of suspicious activity.
  • Developed a user-friendly web interface for uploading footage and receiving anomaly detection results.

Main Results:

  • The AI-driven system successfully spots anomalous activities in surveillance footage without manual intervention.
  • The system accurately identifies the type of anomaly and the specific time frame of its occurrence.
  • Demonstrated a significant improvement in the speed and efficiency of detecting suspicious activities compared to manual monitoring.

Conclusions:

  • The developed AI system offers a more effective and efficient solution for surveillance in high-stakes environments.
  • Automated anomaly detection using deep learning and transformer models significantly enhances security.
  • The system provides proactive, accurate, and stress-free monitoring, reducing the risk of critical events being missed.