Advance Deep Learning Approaches for Optical Flow Estimation

In recent years, deep learning approaches have revolutionized optical flow estimation, providing significant improvements over classical methods. This chapter presents an in-depth exploration of advanced deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models for optical flow estimation. We examine how these approaches address the limitations of traditional variational methods, particularly in challenging environments such as occlusions, motion blur, and adverse weather conditions. Special attention is given to models such as FlowNet, PWC-Net, and RAFT, which have achieved state-of-the-art performance. Additionally, we explore how the integration of unsupervised learning and self-supervised learning techniques can further enhance optical flow estimation without relying heavily on labeled data.