Going deeper into third-person action anticipation - Rowlands, Sareh (Open Science Journal, 2023)
Analysing human actions in videos is gaining a great deal of interest in the field of computer vision. This paper explores and reviews different deep learning techniques used in third-person action anticipation. The task of action anticipation is divided into feature extraction and a predictive model for many architectures. This paper outlines a project plan for action anticipation in the third person using step-based activity. We will use several data sets to compare some of these different architectures based on their prediction accuracy and ability to predict actions in varying time frames.