title: Watch-n-Patch: Unsupervised Learning of Actions and Relations.
abstract: There is a large variation in the activities that humans perform in their everyday lives. We consider modeling these composite human activities which comprises multiple basic level actions in a completely unsupervised setting. Our model learns high-level co-occurrence and temporal relations between the actions. We consider the video as a sequence of short-term action clips, which contains human-words and object-words. An activity is about a set of action-topics and object-topics indicating which actions are present and which objects are interacting with. We then propose a new probabilistic model relating the words and the topics. It allows us to model long-range action relations that commonly exist in the composite activities, which is challenging in previous works. We apply our model to the unsupervised action segmentation and clustering, and to a novel application that detects forgotten actions, which we call action patching. For evaluation, we contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacting with different objects. Moreover, we develop a robotic system that watches and reminds people using our action patching algorithm. Our robotic setup can be easily deployed on any assistive robots.
authors.forename: Chenxia
authors.initials: C
authors.lastname: Wu
authors.forename: Jiemi
authors.initials: J
authors.lastname: Zhang
authors.forename: Ozan
authors.initials: O
authors.lastname: Sener
authors.forename: Bart
authors.initials: B
authors.lastname: Selman
authors.forename: Silvio
authors.initials: S
authors.lastname: Savarese
authors.forename: Ashutosh
authors.initials: A
authors.lastname: Saxena