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Real-time gun detection in CCTV: An open problem.

Jose L Salazar González1, Carlos Zaccaro1, Juan A Álvarez-García1

  • 1Dpto. de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, 41012, Sevilla, Spain.

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Summary

This study introduces a new dataset and Faster R-CNN model for detecting weapons in real-time on CCTV footage. The approach significantly improves weapon detection accuracy, benefiting security and counter-terrorism efforts.

Keywords:
Convolutional neural networkData augmentationDeep learningFeature Pyramid NetworkSynthetic dataWeapon detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Security Systems

Background:

  • Small object detection remains a challenge for current object detectors.
  • Autonomous weapons detection in Closed-circuit television (CCTV) is crucial for security and risk mitigation.

Purpose of the Study:

  • To develop an effective weapon detection model for quasi real-time CCTV applications.
  • To evaluate the impact of synthetic datasets on training weapon detection systems.

Main Methods:

  • Applied Faster R-CNN with Feature Pyramid Network and ResNet-50 to real and synthetic CCTV data.
  • Utilized a two-stage training approach for enhanced detection.
  • Achieved an inference time of 90 ms on an NVIDIA GeForce GTX-1080Ti.

Main Results:

  • Developed a weapon detection model with improved state-of-the-art performance.
  • Demonstrated the effectiveness of synthetic datasets in augmenting real-world CCTV data.
  • Identified current limitations in automated weapon detection systems.

Conclusions:

  • The proposed method offers a viable solution for quasi real-time weapon detection in CCTV.
  • Synthetic data generation is a valuable technique for improving the robustness of weapon detection models.
  • Further research is needed to address the inherent limitations of current detection systems.