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.

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:

1. 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.

2. Install segmentizer library

   Run python3 setup.py install

   Note: setup.py can be found inside the folder "framework"

3. 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

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

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.

[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