Estimating optical flow: A comprehensive review of the state of the art
Andrea Alfarano, Luca Maiano, Lorenzo Papa, Irene Amerini
Abstract
Optical flow estimation is a crucial task in computer vision that provides low-level motion information. Despite recent advances, real-world applications still present significant challenges. This survey provides an overview of optical flow techniques and their application. For a comprehensive review, this survey covers both classical frameworks and the latest AI-based techniques. In doing so, we highlight the limitations of current benchmarks and metrics, underscoring the need for more representative datasets and comprehensive evaluation methods. The survey also highlights the importance of integrating industry knowledge and adopting training practices optimized for deep learning-based models. By addressing these issues, future research can aid the development of robust and efficient optical flow methods that can effectively address real-world scenarios. • Investigating integration of traditional techniques in modern models. • Surveying key challenges of optical flow in real-world applications. • Offering the most comprehensive survey of datasets for optical flow. • Presenting a complete overview of Classical and Modern Optical Flow methods. • Highlighting crucial open questions, paving way for future research.