Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Study on Machine Learning and Deep Learning Methods for Human Action Recognition

Version 1 : Received: 7 May 2020 / Approved: 9 May 2020 / Online: 9 May 2020 (03:56:56 CEST)

How to cite: Rajendran, G.; Thomas Lee, O.; Gopi, A.; jose, J.; Gautham, N. Study on Machine Learning and Deep Learning Methods for Human Action Recognition. Preprints 2020, 2020050146 (doi: 10.20944/preprints202005.0146.v1). Rajendran, G.; Thomas Lee, O.; Gopi, A.; jose, J.; Gautham, N. Study on Machine Learning and Deep Learning Methods for Human Action Recognition. Preprints 2020, 2020050146 (doi: 10.20944/preprints202005.0146.v1).

Abstract

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

Subject Areas

human action recognition; machine learning; action feature representation; action classification; deep learning

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