Dynamic semantic segmentation

Dynamic semantic segmentation is a computer vision task that involves the automatic labeling of each pixel in an image with a semantic class. Unlike traditional semantic segmentation, dynamic semantic segmentation is able to handle videos or sequences of images, where the objects and their relationships with the surrounding environment change over time.

Dynamic semantic segmentation can be used in a variety of applications, such as autonomous driving, video surveillance, and robotics. In autonomous driving, for instance, dynamic semantic segmentation can help the vehicle identify and track moving objects, such as other vehicles, pedestrians, and cyclists, and make decisions based on their movements and behaviors in real-time.

To perform dynamic semantic segmentation, deep learning models such as convolutional neural networks (CNNs) are typically used. These models are trained on large datasets of labeled images and videos, and are able to learn complex features and representations that enable them to accurately classify each pixel in an image or frame of a video.