  This paper addresses the critical need for online action representation,
which is essential for various applications like rehabilitation, surveillance,
etc. The task can be defined as representation of actions as soon as they
happen in a streaming video without access to video frames in the future. Most
of the existing methods use predefined window sizes for video segments, which
is a restrictive assumption on the dynamics. The proposed method employs a
change detection algorithm to automatically segment action sequences, which
form meaningful sub-actions and subsequently fit symbolic generative motion
programs to the clipped segments. We determine the start time and end time of
segments using change detection followed by a piece-wise linear fit algorithm
on joint angle and bone length sequences. Domain-specific symbolic primitives
are fit to pose keypoint trajectories of those extracted segments in order to
obtain a higher level semantic representation. Since this representation is
part-based, it is complementary to the compositional nature of human actions,
i.e., a complex activity can be broken down into elementary sub-actions. We
show the effectiveness of this representation in the downstream task of class
agnostic repetition detection. We propose a repetition counting algorithm based
on consecutive similarity matching of primitives, which can do online
repetition counting. We also compare the results with a similar but offline
repetition counting algorithm. The results of the experiments demonstrate that,
despite operating online, the proposed method performs better or on par with
the existing method.
