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Updated: Jun 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model.

Rashmi Chaudhary1, Manoj Kumar2

  • 1University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi, India.

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|September 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computer vision method for crowd anomaly detection, achieving 97.28% accuracy. The approach uses visual attention and deep learning, optimized with a unique algorithm for enhanced surveillance.

Keywords:
Anomaly detectionbilateral filteringdeep learning, deep Maxout classifierhybrid optimizationvisual attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Surveillance video analysis is labor-intensive and challenging due to complex crowd behaviors.
  • Automated anomaly detection in crowds is crucial for public safety and security.

Purpose of the Study:

  • To develop an advanced computer vision method for accurate crowd anomaly detection.
  • To improve the efficiency and reliability of identifying unusual behaviors in crowded environments.

Main Methods:

  • A two-step approach: Visual Attention Detection using an Enhanced Bilateral Texture-Based Methodology and Anomaly Detection via an Optimized Deep Maxout Network.
  • The model is trained using the Battle Royale Coalesced Atom Search Optimization (BRCASO) algorithm for optimal weight tuning.
  • Implementation in Python for practical application and performance evaluation.

Main Results:

  • The proposed method achieved a detection accuracy of 97.28% at a 90% learning rate.
  • Outperformed traditional methods, including ASO (90.56%), BMO (91.39%), BES (88.63%), BRO (86.98%), and FFLY (89.59%).

Conclusions:

  • The developed crowd anomaly detection system demonstrates superior accuracy and reliability.
  • The combination of visual attention, deep learning, and advanced optimization offers a significant advancement in surveillance technology.