Optical Flow

Optical flow, the estimation of apparent motion in image sequences, is a fundamental computer vision task aiming to understand and represent movement in visual data. Current research emphasizes improving accuracy and efficiency in challenging conditions like adverse weather and low-light, often employing deep learning architectures such as recurrent neural networks, transformers, and convolutional neural networks, sometimes integrated with other modalities like depth or inertial measurements. This field is crucial for numerous applications, including autonomous driving, robotics, video processing (e.g., inpainting, deblurring), and medical image analysis, with ongoing efforts focused on developing more robust, efficient, and generalizable methods.