A Comparative Review of Recent Kinect-Based Action Recognition Algorithms

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

A Comparative Review of Recent Kinect-Based Action Recognition Algorithms

By: 
Lei Wang; Du Q. Huynh; Piotr Koniusz

Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare 10 recent Kinect-based algorithms for both cross-subject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that the skeleton-based features are more robust for cross-view recognition than the depth-based features, and that the deep learning features are suitable for large datasets.

SPS on Twitter

  • is now accepting papers through Wednesday, 13 December! The conference heads to London in July and is see… https://t.co/Z1KqYXWtGY
  • The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special Issue on Domain Enri… https://t.co/ZX6U9oGCZe
  • New webinar alert! Join us this Tuesday, 10 December for "Toward Efficient and Flexible CNN-based Denoising in Phot… https://t.co/FiizKIEfAO

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar