  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
people and reminds people by applying our action patching algorithm. Our
robotic setup can be easily deployed on any assistive robot.
