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.

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

Made By

• Harshita Lakhotiya
• Deepak Yadav
• Rajat Shenoy