Adaptive foreground-background segmentation using GMMs

This project illustrates how to apply adaptive background-segmentation for videos.
More precisely, I implement the method by proposed by Stauffer and Grimson in their paper "Adaptive background mixture models for real-time tracking" [1]. Performance evaluation was done based on different indoor scenes from the LASIESTA [2] dataset.

This project was also my capstone project for the Udacity Machine Learning Nanodegree. So, if you want interested in a more detailed explanation of the project, please checkout out my project report.

Note: The developed Python code does not allow to segment videos in real-time. An efficient real-time implementation needs to be done in C / C++. However, it's a great starting point if you just want to see and understand the basic concept.

How to segment our own videos?

You got your own video you want to segment? This is easy.
I've built a small library that takes individual video frames as input and returns a segmented frame as output. To install and use the library simple execute the following steps:

Check or install dependencies

Run the code please make sure that you have Numpy and Numba installed on our system. Numba speeds up required matrix computations by means of just-in-time compilation.
Install segmentizer library

Run python3 setup.py install

Note: setup.py can be found inside the folder "framework"
Fit and train our own model
```
from segmentizer import Segmentizer
	
segmentizer = Segmentizer(frame_width, frame_height)
segmented_frame = segmentizer.fit_and_predict(our_frame)
```
Note: To segment the whole video simply path all frames to fit_and_predict iteratively. The method returns a 2D Python list object with binary values where
- True: Background pixel
- False: Foreground pixel

The implementation

If you are curious and you want to see the actual implementation, I recommend to take a look at the classes RGBPixelProcess and IIDGaussian.

Good to know

The original paper is not very detailed in terms of the original implementation. I implemented the code based on my own understanding of the paper and therefore it might not coincide with the one from the original paper.

References

[1] Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking. Proc IEEE Conf on Computer Vision and Pattern Recognition (CVPR 1999) 1999; 246-252.
[2] http://www.gti.ssr.upm.es/data/LASIESTA

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