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Object Detection for Crime Scene Evidence Analysis Using Deep Learning

  • Surajit SaikiaEmail author
  • E. Fidalgo
  • Enrique Alegre
  • Laura Fernández-Robles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)

Abstract

Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU.

Keywords

Object detection Convolutional neural network Deep learning Video surveillance Crime scenes Cyber-security 

Notes

Acknowledgement

This research was funded by the framework agreement between the University of León and INCIBE (Spanish National Cybersecurity Institute) under addendum 22. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Surajit Saikia
    • 1
    • 2
    Email author
  • E. Fidalgo
    • 1
    • 2
  • Enrique Alegre
    • 1
    • 2
  • Laura Fernández-Robles
    • 2
    • 3
  1. 1.Department of Electrical, Systems and AutomationUniversity of LeónLeónSpain
  2. 2.INCIBE (Spanish National Cybersecurity Institute)LeónSpain
  3. 3.Department of Mechanical, Informatics and Aerospace EngineeringUniversity of LeónLeónSpain

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