  Spatio-temporal action detection (STAD) aims to classify the actions present
in a video and localize them in space and time. It has become a particularly
active area of research in computer vision because of its explosively emerging
real-world applications, such as autonomous driving, visual surveillance,
entertainment, etc. Many efforts have been devoted in recent years to building
a robust and effective framework for STAD. This paper provides a comprehensive
review of the state-of-the-art deep learning-based methods for STAD. Firstly, a
taxonomy is developed to organize these methods. Next, the linking algorithms,
which aim to associate the frame- or clip-level detection results together to
form action tubes, are reviewed. Then, the commonly used benchmark datasets and
evaluation metrics are introduced, and the performance of state-of-the-art
models is compared. At last, this paper is concluded, and a set of potential
research directions of STAD are discussed.
