Advancements in Deep Learning-Based Optical Flow Estimation: A Comprehensive Review of Models and Techniques

Optical flow estimation is a fundamental task in computer vision as it plays a pivotal role in numerous applications, including but not limited to motion analysis, video compression, and object tracking. Traditional methods for optical flow estimation often face challenges in handling complex motion patterns and variations in scene structure. In recent years, deep learning has emerged as a powerful paradigm for addressing these challenges, enabling end to end learning of complex motion representations directly from image data. This review article provides a comprehensive overview of recent advancements in deep learning-based optical flow estimation over the past six years. We present an in-depth analysis of 8 state-of-the-art models, including FlowNet, FlowNet 2.0, SpyNet, PWC-Net, and RAFT, discussing their architectures, strengths, weaknesses, and performance on benchmark datasets. Additionally, we conduct a comparative analysis of these models based on criteria such as accuracy, computational efficiency, robustness, and generalization. Finally, we discuss future research directions and challenges in deep learning-based optical flow estimation, highlighting opportunities for further advancements in the field. This review serves as a valuable resource for researchers and practitioners interested in understanding the current landscape of deep learning techniques for optical flow estimation and charting the course for future research endeavors.