A Survey on Deep Learning-based Spatio-temporal Action Detection
It addresses the need for robust action detection in videos for applications like autonomous driving and surveillance, but it is a survey paper, so it is incremental by summarizing existing work.
This paper provides a comprehensive review of deep learning-based methods for spatio-temporal action detection, comparing state-of-the-art models on benchmark datasets and discussing potential research directions.
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.