Traditional and modern strategies for optical flow: an investigation

• Published in: SN applied sciences 2021-03, Vol.3 (3), p.289, Article 289
• Main Authors: Shah, Syed Tafseer Haider, Xuezhi, Xiang
• Format: Article
• Language: English
• Subjects: Accuracy; Applied and Technical Physics; Artificial neural networks; Cameras; Chemistry/Food Science; Computer vision; Deep learning; Earth Sciences; Engineering; Engineering: Digital Image Processing; Environment; Image processing; Materials Science; Neural networks; Optical communication; Optical flow (image analysis); Pedestrians; Real time; Review Paper; Solar physics; Teaching methods
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-021-04227-x

Optical Flow Estimation is an essential component for many image processing techniques. This field of research in computer vision has seen an amazing development in recent years. In particular, the introduction of Convolutional Neural Networks for optical flow estimation has shifted the paradigm of research from the classical traditional approach to deep learning side. At present, state of the art techniques for optical flow are based on convolutional neural networks and almost all top performing methods incorporate deep learning architectures in their schemes. This paper presents a brief analysis of optical flow estimation techniques and highlights most recent developments in this field. A comparison of the majority of pertinent traditional and deep learning methodologies has been undertaken resulting the detailed establishment of the respective advantages and disadvantages of the traditional and deep learning categories. An insight is provided into the significant factors that affect the success or failure of the two classes of optical flow estimation. In establishing the foremost existing and inherent challenges with traditional and deep learning schemes, probable solutions have been proposed indeed.