Machine Learning Engineer Nanodegree

:warning: This project is not maintained anymore :warning:

Capstone Project - Human Activity Recognition

Recognizing human activities using Deep Learning

View the project notebook here - Link to Jupyter Notebook

Dataset

There are a total of 599 videos, with each category having 100 videos (with the exception of Handclapping having 99 videos).

All the videos were captured at 25fps frame rate. Each video has a spatial resolution of 160x120 pixels.

Instructions

Clone the repository and navigate to the downloaded folder.

    git clone https://github.com/MrinalJain17/Human-Activity-Recognition.git
    cd Human-Activity-Recognition

Unzip the compressed data files and store in the format as mentioned here
- Use the helper function download_files() present in data_utils.py as follows to do this in your current working directory automatically. (The function will delete the compressed files after they are successfully extracted)
```
    import data_utils

    data_utils.download_files()
```
The following file is corrupted which gives an error when being loaded. Delete it before proceeding.

'person01_boxing_d4_uncomp.avi' (present in Data/Boxing/)

In order to read the videos, there is a helper class Videos in utils.py.

    import numpy as np
    from utils import Videos

    reader = Videos(target_size=(128, 128), 
            to_gray=True, 
                    max_frames=40, 
                    extract_frames='first', 
                    required_fps=5, 
                    normalize_pixels=(-1, 1))

    videos = reader.read_videos(video_absolute_paths)

Refer the code for a detailed documentation.
This utility is being maintained in a seperate repository here

Run the following command to view the project notebook:

    jupyter notebook human_activity_recognition.ipynb

Requirements

Python 3.x (preferably from the Anaconda Distribution)

Install FFmpeg on your machine

For Linux:

    $ sudo apt-get update
    $ sudo apt-get install libav-tools

For Windows or MAC/OSX:
Download the required binaries from here. Extract the zip file and add the location of binaries to the PATH variable

Additional Libraries:

Scikit-video
```
    pip install sk-video
```
Tensorflow
```
    pip install tensorflow
```
For GPU support or a custom installation, follow the instructions given on the Tensorflow website.
Keras
```
    pip install keras
```
tqdm - Required for displaying the progress bar.
```
    pip install tqdm
```

These libraries will be required for successful execution of the project files.

MrinalJain17 / Human-Activity-Recognition

readme