Optical Flow Field

Optical flow fields, representing the motion of pixels in a video sequence, are crucial for various computer vision tasks. Current research focuses on improving the accuracy and efficiency of optical flow estimation and manipulation, employing techniques like deep learning (particularly convolutional neural networks and transformers), partial differential equations, and Expectation-Maximization algorithms. These advancements are driving progress in applications such as action detection in videos, autonomous driving (particularly motion detection), and image inpainting, highlighting the importance of robust and efficient optical flow processing. The development of tools and frameworks, like the Python library Oflib, further facilitates research and application development in this field.