CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Vision based Traffic Police Hand Signal Recognition in Surveillance Video - A Survey

by R. Sathya, M. Kalaiselvi Geetha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 9
Year of Publication: 2013
Authors: R. Sathya, M. Kalaiselvi Geetha
10.5120/14037-2192

R. Sathya, M. Kalaiselvi Geetha . Vision based Traffic Police Hand Signal Recognition in Surveillance Video - A Survey. International Journal of Computer Applications. 81, 9 ( November 2013), 1-10. DOI=10.5120/14037-2192

@article{ 10.5120/14037-2192,
author = { R. Sathya, M. Kalaiselvi Geetha },
title = { Vision based Traffic Police Hand Signal Recognition in Surveillance Video - A Survey },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 9 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number9/14037-2192/ },
doi = { 10.5120/14037-2192 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:35.736210+05:30
%A R. Sathya
%A M. Kalaiselvi Geetha
%T Vision based Traffic Police Hand Signal Recognition in Surveillance Video - A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 9
%P 1-10
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Human gesture recognition has become a very important topic in computer vision. The purpose of this survey is to provide a detailed overview and categories of current issues and trends. The recognition of human hand gesture movement can be performed at various level of abstraction. This survey concentrate on approaches that aim on recognizing traffic police hand signals. Many application and algorithms were discussed with the recognition framework. General overview of an traffic control gestures and its various applications where discussed in this paper. Most of the recognition system uses the benchmark datasets like KTH, Weizmann. some other datasets were used by the action recognition system. In this paper image representation,action representation, human action detection, feature extraction and human action recognition were also discussed.

References
  1. Yang Wang, Hao Jiang, Mark, S. , Drew, Ze-Nian Li, and Greg Mori. june, 2006. Unsupervised discovery of action classes, In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR06), Vol. 2, pp. 1654-1661.
  2. Nazli Ikizler, Ramazan, G. , Cinbis, Selen Pehlivan, and Pinar Duygulu. december, 2008. Recognizing actions from still images, In: Proceedings of the International Conference on Pattern Recognition (ICPR08), Tampa, FL, pp. 1-4.
  3. Yan Ke, Rahul Sukthankar, and Martial Hebert. october, 2007. Event detection in crowded videos, In: Proceedings of the International Conference On Computer Vision (ICCV07), Rio de Janeiro, Brazil, pp. 1-8.
  4. Laptev, I. , Marszalek, M. , Schmid, C. and Rozenfeld, B. 2008. Learning realistic human actions from movies, In: Conference on Computer Vision and Pattern Recognition, pp. 1-8.
  5. Munder, S. and Gavrila, D. M. november, 2006. An Experimental Study on Pedestrian Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, pp. 1863–1868.
  6. Enzweiler, M. , Eigenstetter, A. , Schiele, B. and Gavrila, D. M. 2010. Multi-Cue Pedestrian Classification with Partial Occlusion Handling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  7. Keller, C. , Enzweiler, M. and Gavrila, D. M. 2011. A New Benchmark for Stereo-based Pedestrian Detection, Proc. of the IEEE Intelligent Vehicles Symposium, pp. 691–696.
  8. Paul, A. Viola, Michael, J. Jones. December, 2001. Rapid object detection using a boosted cascade of simple features, In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR01), Vol. 1, pp. 511-518.
  9. Willems, G. Tuytelaars, T. and Van Gool, L. 2008. An efficient dense and scale-invariant spatio-temporal interest point detector, In ECCV 2, Volume 5305 of letecture notes in computer science, pp. 650–663.
  10. Klser, A. Marszaek, M. and Schmid, C. September, 2008. A spatio-temporal descriptor based on 3Dgradients, In BMVC, pp. 995–1004.
  11. Fan Guo, Zixing Cai, Jin Tang. 2010. Chinese Traffic Police Gesture Recognition in Complex Scene, International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11.
  12. Starner, T. , Weaver, J. and Pentland, A. 2002. Real-time American Sign Language recognition using desk and wearable computer based video, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20(12).
  13. Kim, J. , Jang, W. and Bien, Z. 1996. A dynamic gesture recognition system for the Korean sign language (KSL), IEEE transactions on systems, man, and cybernetics-part B: Cybernetics, vol. 26(2), pp. 354–359.
  14. Liang, R. and Ouhyoung, M. 1998. A real-time continuous gesture recognition system for sign language, IEEE Third International Conference on Automatic Face and Gesture Recognition Proceedings, pp. 558–567.
  15. Maraqa, M. and Abu-Zaiter, R. 2008. Recognition of Arabic Sign Language (ArSL) using recurrent neural networks, IEEE First International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008, pp. 478– 48.
  16. Murakami, K. and Taguchi, H. 1999. Gesture recognition using recurrent neural networks. ACM, Proceedings of the SIGCHI conference on Human factors in computing systems: Reaching through technologyCHI '91, pp. 237–242.
  17. Pavlovic, V. I. , Sharma, R. and Huang, T. s. 1997 Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19(7), pp. 677–695.
  18. Ben Wang, Tao Yuan. 2008. Traffic Police Gesture Recognition using Accelerometers, Vol. 1, pp. 4244–2581.
Index Terms

Computer Science
Information Sciences

Keywords

Computer vision traffic control gesture hand action feature extraction Activity recognition.

Powered by PhDFocusTM