A study of action recognition problems: Dataset and architectures perspectives

Abstract

Action recognition field has recently grown dramatically due to its importance in many applications like smart surveillance, human–computer interaction, assisting aged citizens or web-video search and retrieval. Many research trials have tackled action recognition as an open problem. Different datasets are built to evaluate architectures variations. In this survey, different action recognition datasets are explored to highlight their ability to evaluate different models. In addition, for each dataset, a usage is proposed based on the content and format of data it includes, the number of classes and challenges it covers. On other hand, another exploration for different architectures is drawn showing the contribution of each of them to handle different action recognition problem challenges and the scientific explanation behind their results. An overall of 21 datasets is covered with 13 architectures that are shallow and deep models.