Temporal Action Detection Methods Based on Deep Learning

Abstract

Temporal action detection is one of the most important and challenging tasks in video analysis. Due to its wide application prospects, it has received extensive attention in recent years. With the development of deep learning, great progress has been made in temporal behavior detection, but there are still many difficulties to be solved, such as accurate proposal generation and high computational cost. In this paper, deep learning-based temporal action detection methods are classified according to full supervision and weak supervision, and then the representative models of the two methods are summarized in detail, and the ideas, advantages and disadvantages of different models and the evolution between different models are analyzed. At the same time, the performance of different models on mainstream datasets is compared. The mainstream dataset and evaluation index used in temporal action detection are introduced in detail, and the calculation method of evaluation index is also elaborated. Finally, through in-depth analysis, the possible future research directions of temporal action detection and the whole review are summarized.