A survey on action and event recognition with detectors for video data

Abstract

In this paper, the development of action and event detectors over the past three decades is summarized. The detectors are divided into 2D detectors, 3D detectors and deep learning detectors according to whether they contain spatial information and whether they use deep learning. This paper briefly introduces the typical detectors of the different types mentioned above, and explains the advantages, disadvantages and characteristics respectively, and compares them. Comparing traditional feature detection methods with ones based on deep learning, we found that the method of first detecting microscopic details such as point, line, surface angle, etc., and then performing action and event recognition is no longer the mainstream of current research. Due to the strong generalization ability, end-to-end action and event recognition methods based on deep learning perform better than traditional methods. Finally, this paper proposes three research directions for action recognition and event recognition based on feature detectors.