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Related Experiment Video

Updated: Jan 12, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Real-time abnormal behaviour detection using energy-efficient YOLO-based framework.

Sreedevi R Krishnan1, P Amudha2,3, S Sivakumari3

  • 1Department of Computer Science and Engineering, Adi Shankara Institute of Engineering and Technology, Kalady, Ernakulam, Kerala, India. sreedevirkrishnan@gmail.com.

Scientific Reports
|November 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI model for detecting abnormal behavior in public spaces using an optimized YOLO network. The system achieves 99.46% accuracy, enhancing public safety through advanced computer vision techniques.

Keywords:
Adam optimizationCNNHistogram equalisationOptimisedYolo

Related Experiment Videos

Last Updated: Jan 12, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Public Safety

Background:

  • Increasing incidents of abnormal behavior in public spaces necessitate advanced detection methods.
  • Traditional surveillance methods are often reactive and insufficient for real-time anomaly identification.
  • Advancements in Artificial Intelligence (AI), particularly Deep Learning and Computer Vision, offer potential solutions for automated anomaly detection.

Purpose of the Study:

  • To develop and evaluate an AI-powered system for the precise detection and analysis of abnormal behavior in public areas.
  • To leverage an optimized You Only Look Once (YOLO) network for enhanced human detection and behavior analysis.
  • To improve the accuracy and efficiency of real-time abnormal behavior identification in surveillance systems.

Main Methods:

  • Utilized an optimized YOLO network integrated with Adam Optimization and histogram equalization for anomaly detection.
  • Implemented temporal tracking of detected objects to identify abnormal behavior patterns over time.
  • Employed refinement techniques to enhance the precision and robustness of the detection model.

Main Results:

  • The proposed model achieved a remarkable accuracy of 99.46% in identifying and analyzing abnormal behavior.
  • The system demonstrated effective human detection and abnormal behavior pattern recognition.
  • Optimization techniques significantly improved the accuracy and efficiency of the detection process.

Conclusions:

  • The AI model provides a highly accurate and efficient solution for detecting abnormal behavior in real-time.
  • The optimized YOLO framework is effective for human detection and anomaly analysis in public spaces.
  • This technology has significant potential for application in various real-time public safety scenarios.