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Final Year Undergraduate Project - Detects abnormal activity in video streams using 3D CNNs and 2D Convolutional LSTMs

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Smart-Detection-of-Abnormalities-in-Video-Footage

Using Convolutional LSTM filters we have developed a model that can identify single instance abnormalities in video footage with an accuracy of 92.68%. Model was trained on our very own dataset.

The paper for our project - https://link.springer.com/chapter/10.1007/978-981-19-8669-7_47

final

A custom dataset was created for this project using a standard CCTV camera.

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Final Year Undergraduate Project - Detects abnormal activity in video streams using 3D CNNs and 2D Convolutional LSTMs

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