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Anomaly-Detection

This project is trained on the UCSD dataset. The code has been kept in modular format, with the following modules -

  1. data_loader.py - Contains two tf.keras.utils.Sequence classes - CNN_train_data_loader and CNN_test_data_loader to load batches of images and labels
  2. paths.py and hyperparams.py - Contains paths and hyperparams needed for the model
  3. logger.py - Logger callback
  4. timer.py- Timer callback
  5. train.py - Entry point for the code; starts the training sequence
  6. model.py - Contains two models - a CNN Autoencoder and a 2-layered LSTM

CNN Autoencoder

A CNN Autoencoder is trained to learn latent space representation of frame images

Stacked LSTM

A 2-layered LSTM is trained to predict if the sequence of frames contains an anomaly. Latent space representation of each frame is passed to the stacked LSTM.