Real-Time Optical Flow Using Neural Networks
From iis-projects
Contents
Short Description
Optical flow data is an ingredient to many complex computer vision systems. There are already quite a few algorithms to determing the optical flow between to frames very well. Some of these approaches use spatial correlation of small image patches, others work by looking at few well-trackable points (corners, edges, ...) and then solve complex optimization problems to find reasonable solutions between these key points.
Evaluation of these algorithms is very time-consuming, often taking many seconds per frame on powerful workstations. This puts real-time applications out of reach, particularly on low-power and mobile platforms, where computer vision is most interesting.
We would like to take an unconventional approach to resolve this problem, training deep convolutional neural networks on
Status: Available
- Looking for 1 Master or 2 semester project students
- Contact: Lukas Cavigelli
Prerequisites
- Knowledge of Matlab and/or C/C++
- Interest in video processing and machine learning
Character
- 20% Theory / Literature Research
- 20% Software Development, 60% FPGA Development & Verification
- or 80% software development
