MultiFlow - Multiple Frame Approach for Optical Flow Estimation Using Deep Learning

A. Bansal

Motion detection, and activity recognition are some of the most prominent tasks in the domain of computer vision. With large scale developments taking place in the field of autonomous driving, and robotics, solving these tasks efficiently with accurate results becomes even more important. Taking inspiration from this challenge, in this thesis, a new architecture is proposed called as, MultiFlow, which aims at obtaining robust optical flow in terms of accuracy and smoothness. This model is based on the works of FlowNet-C and it estimates optical flow by taking consecutive multiple image frames as inputs and computing the correlation between these image frames. The model will be evaluated against the state-of-the-art neural network models in the field of optical flow estimation on the MPI-Sintel dataset.