Bio-Inspired Computer Vision: Optical flow estimation using space-time separable filters

This project is one part of the module: Bio-inspired computer vision and was implemented by Bing Li under Prof. Dr. Marianne Maertens and Prof. Guillermo Gallego.

This is the code used to produce a filter part(Section 2.2 and Section 3.1) of the results published in [1] .But generating these result needs pre-knowledge included in Section 3 [2] and in Section 3.1 [3]

The repository contains the following:

-Python code: folder:src

1.optical_flow.ipynb

a.definitions of spatial and temporal filters
b. visualization of filters above in 2d or 3d view
c.implementation of equation (Eq 23) in ¹[@tschechneBioInspiredOpticFlow2014] and visualization
d.using aggregation to calculate the velocity at each pixel for optical flow based on separable filters and visualization(Eq.33 ¹)
e. .npy files are the intermediate files

2.util.py

some useful tools for loading data , the time consuming calculation, plotting tools and etc,this code is provided by Cedric Scheerlinck

-Dataset:

This is the dataset which is used in this project.For this dataset, only event.txt is used.

-Figures:

the generated figures are saved in the folder: output_figures

-Slides:

This is the folder contains the slides which explains some general ideas of this project.

Dependencies of this code

numpy,
matiplotlib,
pandas,
opencv,
scipy

References

• [1] Brosch Tobias, Tschechne Stephan, Neumann Heiko,On event-based optical flow detection

• [2] Tschechne, Stephan and Sailer, Roman and Neumann,Heiko.Bio-Inspired Optic Flow from Event-Based Neuromorphic Sensor Input

• [3]Tschechne, Stephan and Brosch, Tobias and Sailer, Roman and von Egloffstein, Nora and Abdul-Kreem, Luma Issa and Neumann, Heiko.On Event-Based Motion Detection and Integration