Title
Human Action Recognition and Prediction: A Survey
Abstract
Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
Year
DOI
Venue
2018
10.1007/s11263-022-01594-9
International Journal of Computer Vision
Keywords
Field
DocType
Action recognition, Action prediction, Video data, Survey
Video retrieval,Computer science,Entertainment,Action recognition,Artificial intelligence,Visual surveillance,Machine learning
Journal
Volume
Issue
ISSN
130
5
0920-5691
Citations 
PageRank 
References 
6
0.43
152
Authors
2
Search Limit
100152
Name
Order
Citations
PageRank
Yu Kong141224.72
Yun Fu24267208.09