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On Wednesday, December 16, Aayush Suresh Bansal will present the results of his master's thesis with the title:

UFCFlow - Optical Flow Estimation using Unsupervised Deep Learning

Abstract:

In recent times, deep learning is used to estimate accurate optical flow in a supervised or unsupervised setting. As opposed to an unsupervised approach, training a Convolutional Neural Network (CNN) for flow estimation in a supervised manner requires large amount of labelled data. However, due to shortage of labelled data such an approach is not feasible and cannot be applied to situations where labelled data is not present. To overcome the problem of labelled data, this thesis focuses on the development and implementation of a CNN model for optical flow estimation in an unsupervised setting . For this purpose, a CNN model will be trained using an unsupervised learning method with the help of proxy ground-truth to aid in the learning process. To guide the learning process, existing and new error functions will be studied and implemented to obtain robust and accurate optical flow. The developed model will be trained on the Sintel, FlyingChairs, and KITTI datasets for flow estimation. The developed model’s performance will be evaluated and compared against state-of-the-art optical flow neural network models on the above datasets.