A Model Net Utilizing Temporal Information for Video Object Segmentation

Dr. Schwing has developed a model with a temporal backbone that allows video object segmentation.  This allows for the model to compare images frame by frame and create a propagated image in order to identify occluded objects more accuractely. The previous method of a single image analysis sometimes caused issues in the object getting lost or misidentified by the algorithms. This technology is particularly useful in autonomous driving features for lane changes, and other maneuvers. It also has potential uses in tracking software for security purposes.