The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with
wearable and contactless sensors aimed at tackling real‐world problems warrants a …

Human action recognition based on skeletons has wide applications in human–computer
interaction and intelligent surveillance. However, view variations and noisy data bring …

Sports data analysis is becoming increasingly large scale, diversified, and shared, but
difficulty persists in rapidly accessing the most crucial information. Previous surveys have …

To understand human behaviors, action recognition based on videos is a common
approach. Compared with image-based action recognition, videos provide much more …

Deep learning has the potential to revolutionize sports performance, with applications
ranging from perception and comprehension to decision. This paper presents a …

Recent progress in sports analytics has been driven by the availability of spatio-temporal
and high level data. Video-based action recognition in sports can significantly contribute to …

Two-stream convolutional neural networks show great promise for action recognition tasks.
However, most two-stream based approaches train the appearance and motion …

Active learning aims to train a classifier as fast as possible with as few labels as possible.
The core element in virtually any active learning strategy is the criterion that measures the …

The ability to identify human actions in uncontrolled surroundings is important for Human–
Computer Interaction (HCI), especially in the sports areas to offer athletes, coaches, and …

Abstract The Sports Action Recognition (SAR) domain is of significant importance in
research with diverse applications ranging from aiding coaches in strategic decision-making …