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An indigenous system design for anomaly detection from CCTV camera feed

License: GNU General Public License v3.0

Python 100.00%

camera cctv-detection digital-image-processing computer-vision python

btp's Introduction

An indigenous system design for anomaly detection from CCTV camera feed.
Check out requirement document for basic understanding of our project.

For contribution clone this repository using

git clone --depth 1 --recursive https://github.com/Hyouteki/BTP-project.git

Important

Models	Maintained	Directory
Anomaly detection using CNN, LSTM, Optical flow, & SNN architectures	✔️	src
Object Detection using YOLO architecture	❌	yolo
Object Detection using TensorflowLite architecture	❌	tflite
Object Detection using InceptionV3 architecture	❌	inception
Anode: Simple GUI application for tinkering with model	✔️	gui
AnodeCLI: Simple CLI application for model inference	✔️	cli

btp's People

Contributors

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tsidren

btp's Issues

research upon "spiking neural network"

References

https://en.wikipedia.org/wiki/Spiking_neural_network

research upon "optical flow"

Reference

https://www.southampton.ac.uk/~msn/book/new_demo/opticalFlow/

Bounding Box

The Bounding Box display was not accurate.

Training loss decreases while validation loss does not; might be because of overfitting

Reference: keras-team/keras#8471

Training loss decreases while validation loss does not and same for accuracy; training accuracy increases while validation accuracy does not grow with the same pace.

Hey can you detect between authorised and unauthorised human intrusion

Like if a person is valid/authorsied person it will not detect intrusion and if the person is intruder it will send email to the security person.

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