Deep Learning for Vision-based Prediction: A Survey
It organizes existing research for researchers and practitioners in applications like autonomous driving and surveillance, but is incremental as it does not introduce new methods.
This survey provides an overview of deep learning approaches for vision-based prediction tasks, categorizing algorithms and highlighting architectures, training methods, and evaluation metrics used in the field over the past five years.
Vision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches. For this purpose, we categorize these algorithms into video prediction, action prediction, trajectory prediction, body motion prediction, and other prediction applications. For each category, we highlight the common architectures, training methods and types of data used. In addition, we discuss the common evaluation metrics and datasets used for vision-based prediction tasks. A database of all the information presented in this survey including, cross-referenced according to papers, datasets and metrics, can be found online at https://github.com/aras62/vision-based-prediction.