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Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization.

Nadia Mumtaz1, Naveed Ejaz1,2, Suliman Aladhadh3

  • 1Department of Computing and Technology, Iqra University, Islamabad Campus, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary

This study introduces control charts for automated video surveillance, enhancing violence detection. The novel deep learning framework integrates spatial and temporal data for improved risk analysis in real-world CCTV systems.

Keywords:
anomaly detectionfight detectionsurveillance video analysisvideo classification

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Process Control

Background:

  • Automated video surveillance systems are crucial in real-world CCTV environments, with a primary focus on enhancing accuracy.
  • Current systems require more attention towards integrating surveillance expert assistance for effective data analysis and rapid decision-making using advanced computer vision algorithms.

Purpose of the Study:

  • To introduce control charts, a process control technique, for the analysis of surveillance video data.
  • To develop a novel deep learning-based violence detection framework that merges control charts with advanced algorithms.
  • To enhance the accuracy and effectiveness of automated surveillance systems by incorporating spatial and temporal information fusion.

Main Methods:

  • A novel deep learning framework was developed for violence detection, integrating control charts for data analysis.
  • The framework uniquely considers both spatial and temporal representations of video data.
  • A multi-scale strategy was employed to fuse spatial information with the temporal dimension of the deep learning model at multiple levels.

Main Results:

  • The proposed technique successfully integrates spatial and temporal information for robust violence detection.
  • Control charts were utilized to maintain a history of surveillance video analysis results, validating risk levels.
  • Experimental results on existing datasets and real-world data confirm the approach's prominence in automated surveillance.

Conclusions:

  • The study presents a novel approach combining control charts and deep learning for automated video surveillance and violence detection.
  • The proposed method offers a significant advancement in analyzing surveillance data, providing pre- and post-analyses of violent events.
  • This research highlights the potential of process control techniques in improving the efficiency and reliability of intelligent surveillance systems.