Contributers : Sree Harsha Nelaturu, Anith Patel, Avyay Sah

DeepHPD is a novel application which uses Deep Learning in order to detect the presence of a human being. This is done by using a combination of Convolutional Neural Networks and OpenCV which along with a Raspberry pi and Basler Dart Camera, which blinks an LED when it detects human presence.

Software Used :

• Tensorflow
• OpenCV
• Numpy
• RPi-GPIO
• CUDA

Weights.

The Tensorflow graph also to be placed in the same folder

Retrained Graph.

The two classes provided by the classifier are:

1. human
2. nohuman

1. Inference.py - Uses weights of trained Convolutional Neural Network for classification
2. LED-Blink.py - Script to be run to use inferred labels from footage in order to detect humans and blink LED
3. retrained_graph.pb - Contains Tensorflow static graph for computation
4. inception - Folder which contains the bottlenecks and weights for the moodel
5. retrained_labels.txt - Text file that contains the labels of the retrained model.

Sample:

[INPUT]:

Footage via Basler DART is taken via OpenCV

[OUTPUT]:

Label : "human" LED Blinks