12 research outputs found
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events
To date, machine learning for human action recognition in video has been
widely implemented in sports activities. Although some studies have been
successful in the past, precision is still the most significant concern. In
this study, we present a high-accuracy framework to automatically clip the
sports video stream by using a three-level prediction algorithm based on two
classical open-source structures, i.e., YOLO-v3 and OpenPose. It is found that
by using a modest amount of sports video training data, our methodology can
perform sports activity highlights clipping accurately. Comparing with the
previous systems, our methodology shows some advantages in accuracy. This study
may serve as a new clipping system to extend the potential applications of the
video summarization in sports field, as well as facilitates the development of
match analysis system.Comment: 18 pages, 3 figures, 4 table
Differentiable Grammars for Videos
This paper proposes a novel algorithm which learns a formal regular grammar
from real-world continuous data, such as videos. Learning latent terminals,
non-terminals, and production rules directly from continuous data allows the
construction of a generative model capturing sequential structures with
multiple possibilities. Our model is fully differentiable, and provides easily
interpretable results which are important in order to understand the learned
structures. It outperforms the state-of-the-art on several challenging datasets
and is more accurate for forecasting future activities in videos. We plan to
open-source the code. https://sites.google.com/view/differentiable-grammar