TRAINING-FREE GENERIC OBJECT DETECTION IN 2-D AND 3-D USING LOCALLY ADAPTIVE REGRESSION KERNELS

摘要

The present invention provides a method of learning-free detection and localization of actions that includes providing a query video action of interest and providing a target video, obtaining at least one query space-time localized steering kernel (3-D LSK) from the query video action of interest and obtaining at least one target 3-D LSK from the target video, determining at least one query feature from the query 3-D LSK and determining at least one target patch feature from the target 3-D LSK, and outputting a resemblance map, where the resemblance map provides a likelihood of a similarity between each the query feature and each target patch feature to output learning-free detection and localization of actions, where the steps of the method are performed by using an appropriately programmed computer.