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crime_detection_system's Introduction

crime_detection_system

  • The system will use different Computer Vision techniques for video analysis.
  • It will monitor CCTV footage for any criminal offenders, violent objects, and suspicious behavior which could lead to crime.
  • SSD Mobilenet Model, an architecture for concealed object detection, is trained for labeling weapons in the frame.
  • The images captured are processed using Face Detection algorithms to identify human faces.
  • Facial Recognition API using libraries in python is implemented to recognize the offenders from criminal records.
  • A ResNet-GRU Model was trained for human behavior analysis which detects suspicious actions.
  • An alert is generated when there are signs of crime and concerned authorities are notified.

Tensorflow FACE-API Build Status Documentation Status

Steps

Requires Conda to run the program
conda create --name --file requirements.txt
Activate the environment
Change the paths in all the files to find the appropriate data
python create_data.py will create the .npy files required for the model to train on
Run the SuspiciousModel.ipynb to create the Suspicious Behaviour Detector Models
python face.py to collect photos of faces to create the criminal database
python encoding.py to create the encoding of the collected photos
python multi.py to run all three models in parallel

Implementation

C1.5_An.Intelligent.Framework.For.Crime.Prediction.Using.Behavioural.Tracking.And.Motion.Analysis.mp4

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