Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Deep Learning based violent protest detection system

  • Received : 2019.02.12
  • Accepted : 2019.03.13
  • Published : 2019.03.29
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Abstract

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

Keywords

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Fig. 1. Related Work: Screenshots

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Fig. 2. Architecture of the proposed system

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Fig. 3. Data augmentation

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Fig. 5. Training

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Fig. 4. Data labeling

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Fig. 6. Early stopping point

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Fig. 8. Detection Result

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Fig. 7. Upload screenshot

Table 1. Related Work

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Table 2. Parameters and results

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Table 3. Data set

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