Next Article in Journal
A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms
Next Article in Special Issue
DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian
Previous Article in Journal
The Interphase Influences on the Particle-Reinforced Composites with Periodic Particle Configuration
Previous Article in Special Issue
Device-Free Indoor Activity Recognition System
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessReview
Appl. Sci. 2017, 7(1), 110; doi:10.3390/app7010110

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

1
School of Computing and Communications Infolab21, Lancaster University, Lancaster LA1 4WA, UK
2
Department of Computer Science, COMSATS Institute of Information Technology, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: José Santamaria
Received: 5 September 2016 / Revised: 26 October 2016 / Accepted: 13 January 2017 / Published: 23 January 2017
(This article belongs to the Special Issue Human Activity Recognition)
View Full-Text   |   Download PDF [31358 KB, uploaded 23 January 2017]   |  

Abstract

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR. View Full-Text
Keywords: computer vision; human action recognition; handcrafted representation; learning-based representation; classification; deep learning; Convolutional Neural Networks; review; survey computer vision; human action recognition; handcrafted representation; learning-based representation; classification; deep learning; Convolutional Neural Networks; review; survey
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Sargano, A.B.; Angelov, P.; Habib, Z. A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition. Appl. Sci. 2017, 7, 110.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top