• Trained and deployed an LSTM based Deep Learning convolutional neural network on 1000 videos dataset, to classify doors and stairs in indoors, with less than 0.01% error, using Python, MobileNetv2, TensorFlow, Keras, Tensorboard, CNN, Google Cloud Platform (GCP), sklearn etc.
• Project Layout-
http://csweb01.csueastbay.edu/~mi7383/CS663/home.html
• Group Members: Richa Khagwal, Subhangi Asati, Maithri Chulikana
• Steps involved in actual implementation-
- Sampling the video: We don’t process every frame, we define a frame generator to create certain sequence length as 40 samples and load the dataset & specify output frames.
- Extracting Features using MobileNetv2-
Step 4: Live Predictions-
-Screenshots of the LiveCaptureResults: