CFP last date
20 May 2024
Reseach Article

A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing

by C. Hemalatha, S. Muruganand, R. Maheswaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 16
Year of Publication: 2014
Authors: C. Hemalatha, S. Muruganand, R. Maheswaran
10.5120/15969-5407

C. Hemalatha, S. Muruganand, R. Maheswaran . A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing. International Journal of Computer Applications. 91, 16 ( April 2014), 38-42. DOI=10.5120/15969-5407

@article{ 10.5120/15969-5407,
author = { C. Hemalatha, S. Muruganand, R. Maheswaran },
title = { A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 16 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number16/15969-5407/ },
doi = { 10.5120/15969-5407 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:57.099953+05:30
%A C. Hemalatha
%A S. Muruganand
%A R. Maheswaran
%T A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 16
%P 38-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object detection, tracking and recognition in real time is a very essential task in computer vision. There are lots of research work have been done in this area. Yet it needs to be accuracy in recognizing object. The most objective of this review is to present an overview of the approaches used and also the challenges involved. In this paper we concentrate on different object detection methods, tracking and recognition methods are discussed.

References
  1. Chih-Hsien Hsia and Jing-MingGuo. , "Efficient modified directional lifting-based discrete wavelet transform for moving object detection", Signal Processing, article in press.
  2. Victoria Yanulevskaya, Jasper Uijlings, Jan-Mark Geusebroek, "Salient object detection: From pixels to segments", Image and Vision Computing Vol. 31, Pg. No. 31–42, 2013.
  3. Pranab Kumar Dhar et al. , "An Efficient Real Time Moving Object Detection Method for Video Surveillance System", International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 3, September, 2012.
  4. Nijad Al-Najdawi et al. , "An Automated Real-Time People Tracking System Based on KLT Features Detection",The International Arab Journal of Information Technology, Vol. 9, No. 1, January 2012.
  5. Kaiqi Huanga,et al. , "A real-time object detecting and tracking system for outdoor night surveillance", Pattern Recognition Vol. 41, Pg. No. 432 – 444, 2008.
  6. Xingwei Yang et al. , "Contour-based object detection as dominant set computation", Pattern Recognition Vol. 45 Pg. No. 1927–1936, 2012.
  7. Carlos Cuevas and Narciso García , "Improved background modeling for real-time spatio-temporal non-parametric moving object detection strategies", Image and Vision Computing Vol. 31, Pg. No. 616–630,2013.
  8. Bahadir Karasulu and Serdar Korukoglu, "Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization", Expert Systems with Applications Vol. 39, Pg. No. 33–43, 2012
  9. Nicholas A. Mandellos et al, "A background subtraction algorithm for detecting and tracking vehicles", Expert Systems with Applications, Vol. 38, Pg. No. 1619–1631, 2011
  10. Ruolin Zhang and Jian Ding, "Object Tracking and Detecting Based on Adaptive Background Subtraction", Procedia Engineering, Vol. 29 Pg. No. 1351 – 1355, 2012
  11. Ling Cai et. al. , "Multi-object detection and tracking by stereo vision", Pattern Recognition Vol. 43, Pg. No. 4028–4041, 2010.
  12. Bangjun Lei and Li-Qun Xu, "Real-time outdoor video surveillance with robust foreground extraction and object tracking via multi-state transition management", Pattern Recognition Letters Vol. 27, Pg. No. 1816–1825, 2006
  13. Ian Fasel, Bret Fortenberry and Javier Movellan, "A generative framework for real time object detection and classification'', Computer Vision and Image Understanding Vol. 98, Pg. No. 182–210, 2005
  14. Christian Goerick and Detlev Noll, "Artificial neural networks in real-time car detection and tracking applications", Pattern Recognition Letters Vol. 17, Pg. No. 335-343, 1996.
  15. Bijan Shoushtarian and Helmut E. Bez, "A practical adaptive approach for dynamic background subtraction using an invariant colour model and object tracking", Pattern Recognition Letters Vol. 26, Pg. No. 5–26, 2005.
  16. Paolo Piccinini et al, "Real-time object detection and localization with SIFT-based clustering", Image and Vision Computing Vol. 30, Pg. No. 573–587, 2012
  17. Gavrila, D. M. and Philomin, V, "Real-time object detection for "smart" vehicles", , The Proceedings of the Seventh IEEE International Conference on Computer Vision Vol. 1, Pg. No. 87 – 93,1999.
  18. Viola, P. and Jones, M , "Rapid object detection using a boosted cascade of simple features" ,Computer Vision and Pattern Recognition, Proceedings of the IEEE Computer Society Conference, Vol. 1,Pg. No. I-511 - I-518, 2001
  19. Anuj Mohan and Tomaso Poggio, "Example-Based Object Detection in Images by Components", IEEE Transactions on pattern analysis and machine intelligence, vol. 23, no. 4, april 2001
  20. Jianxin Wu et al, "C4: A Real-time Object Detection Framework", IEEE transactions on image processing, June 15, 2013
  21. Woo-Han Yun, Sung Yang Bang and Daijin Kim, "Real-time object recognition using relational dependency based on graphical model", Pattern Recognition Vol. 41, Pg . No. 742 – 753, 2008.
  22. Christian Wohler, "Autonomous in situ training of classification modules in real-time vision systems and its application to pedestrian recognition", Pattern Recognition Letters Vol. 23, Pg. No. 1263–1270, 2002
  23. Markus Ulrich, Carsten Steger and Albert Baumgartner, "Real-time object recognition using a modified generalized Hough transform", Pattern Recognition Vol. 36, Pg. No. 2557 – 2570, 2003.
  24. Nicoletta Noceti et al, "Spatio-temporal constraints for on-line 3D object recognition in videos", Computer Vision and Image Understanding Vol. 113 Pg. No. 1198–1209, 2009.
  25. Lowe, D. G, "Object recognition from local scale-invariant features", Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on Computer Vision, Vol. 2, Pg. No. 1150 – 1157, 1999.
  26. Lowe, D. G, " Local feature view clustering for 3D object recognition", Computer Vision and Pattern Recognition Proceedings of the 2001 IEEE Conference on Computer Society , Vol. 1, Pg. No. I-682 - I-688, 2001
  27. S. Belongieet al, "Shape Matching and Object Recognition Using Shape Contexts", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24 Issue 4, Pg. No. 509-522, April 2002
  28. Mirmehdi, M. et al, "Feedback control strategies for object recognition", IEEE Transactions on Image Processing, Vol. 8, Issue: 8 , Pg. No. 1084 – 1101
  29. Hui Chen and Bir Bhanu, "Efficient Recognition of Highly Similar 3D Objects in Range Images", IEEE Transactions on Pattern analysis and machine intelligence, vol. 31, no. 1, Pg. No. 172-179, January 2009.
  30. Dirk Walther et al, "Selective visual attention enables learning and recognition of multiple objects in cluttered scenes", Computer Vision and Image Understanding Vol. 100, Pg. No. 41–63, 2005.
  31. Manuele Bicego et al, "A Hidden Markov Model approach for appearance-based 3D object recognition", Pattern Recognition Letters Vol. 26, Pg. No. 2588–2599, 2005.
  32. Kirt Lillywhite et al, "A feature construction method for general object recognition", Pattern Recognition Vol. 46, Pg. No. 3300–3314, 2013.
  33. C. Wohler and J. K. Anlauf , " real time object recognition on image sequences with adaptable time delay neural network algorithm- applications for autonomous vehicles", image and computer vision, Vol. 19, Pg. No. 593-618, 2001
  34. Mahmoud Meribout, Mamoru Nakanishi and Takeshi Ogura, "A parallel algorithm for real-time object recognition", Pattern Recognition, vol. 35, Pg. No. 1917–1931, 2002
  35. Young, S. S et al, "Object recognition using multilayer Hopfield neural network", IEEE Transactions on Image Processing Vol. 6 , Issue. 3 , Pg. No. 357 - 372 ,1997
Index Terms

Computer Science
Information Sciences

Keywords

object detection object tracking object recognition survey