Semantic Fast-Forwarding for Video Training Set Construction.

We introduce the concept of semantic fast-forwarding of video streams for efficient labeling of training data for activity recognition. We show that this concept can be realized by combining deep learning within individual frames, with spatial and temporal entity-relationship reasoning about detected objects. We describe a prototype that implements this concept, and present preliminary experimental results on its feasibility and value.