Human Action Recognition and Prediction: A Survey

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 (p...

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Vydané v:International journal of computer vision Ročník 130; číslo 5; s. 1366 - 1401
Hlavní autori: Kong, Yu, Fu, Yun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.05.2022
Springer
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Computer Imaging
Computer Science
Computer vision
Datasets
Human activity recognition
Human acts
Human behavior
Human motion
Image Processing and Computer Vision
Machine learning
Machine vision
Pattern Recognition
Pattern Recognition and Graphics
Predictions
Surveillance
Surveys
Vision
Vision systems
Action recognition
Action prediction
Survey
Video data
ISSN:0920-5691, 1573-1405
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-022-01594-9