CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Analysis of Target Tracking Algorithm in Thermal Imagery

by Umesh Gupta, Maitreyee Dutta, Mahesh Vadhavaniya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 16
Year of Publication: 2013
Authors: Umesh Gupta, Maitreyee Dutta, Mahesh Vadhavaniya
10.5120/12443-9140

Umesh Gupta, Maitreyee Dutta, Mahesh Vadhavaniya . Analysis of Target Tracking Algorithm in Thermal Imagery. International Journal of Computer Applications. 71, 16 ( June 2013), 34-41. DOI=10.5120/12443-9140

@article{ 10.5120/12443-9140,
author = { Umesh Gupta, Maitreyee Dutta, Mahesh Vadhavaniya },
title = { Analysis of Target Tracking Algorithm in Thermal Imagery },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 16 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number16/12443-9140/ },
doi = { 10.5120/12443-9140 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:35:45.686166+05:30
%A Umesh Gupta
%A Maitreyee Dutta
%A Mahesh Vadhavaniya
%T Analysis of Target Tracking Algorithm in Thermal Imagery
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 16
%P 34-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Target tracking plays a vital role in the development of battlefield surveillance, airspace surveillance and Border Patrolling. The rapid uses of infrared imagery in target tracking prevents from a wide range of attacks in border security, sea shore security. Infrared imagery is an effective method to cluster heat generating targets and it can penetrate fog, haze, dust, smoke, snow, rain and extreme darkness operate at day and night. Infrared imagery is one of the major and efficient defensive medium in surveillance and monitoring activity. In this paper, an introduction of target tracking algorithms in infrared imagery is discussed and three detection algorithms such as single Reference Frame, Moving Average and Temporal Median Filter with tracking algorithm are implemented and analyzed on multiple targets dataset. This will open the new area for the researcher in the research field of security.

References
  1. Chaohui, Z. , "An improved moving object detection algorithm based on frame difference and edge detection. " IEEE Fourth International Conference on Image and Graphics, 2007.
  2. Mao-Hsiung H. , Jeng-Shyang P. and Chaur-Heh H. , "Speed Up Temporal Median Filter for Background Subtraction," First International Conference on Pervasive Computing Signal Processing and Applications (PCSPA), pp. 297,300, Sept. 2010.
  3. Bashir, F. and Fatih P. , "Performance evaluation of object detection and tracking systems. " In PETS 6, 2006.
  4. Nascimento, J. and Jorge S. M. , "New performance evaluation metrics for object detection algorithms. " Proceedings of IEEE PETS Workshop. 2004.
  5. Mariano and Vladimir Y. , et al. "Performance evaluation of object detection algorithms. " IEEE 16th International Conference on Pattern Recognition, Vol. 3, 2002.
  6. Cmaniciu, R. and Meer, "Kernel-based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, May 2003.
  7. Yilmaz, A. , Omar J. , and Mubarak S. "Object tracking: A survey. "Acm Computing Surveys (CSUR), 2006.
  8. http://www. cse. ohio-state. edu/otcbvs-bench (accessed on 20 Jan 2013).
  9. Elhabian, S. , Khaled, S. , and Sumaya "Moving object detection in spatial domain using background removal techniques-state-of-art. " Recent patents on computer science, 2008
  10. Majid, R. , Jamali, G. , Frizado, B. , "Avian detection & tracking algorithm using infrared imaging," IEEE International Conference on Electro/Information Technology (EIT), pp. 1-4, 2012.
  11. McIvor, "Background Subtraction Techniques," Proc. of Image and Vision Computing, 2000.
  12. Piccardi, "Background subtraction techniques: a review," in Proc. IEEE International Conference Systems, Man, Cybernetics, pp. 3099-3104, 2004.
  13. Welch, G. and Gary B. , "An introduction to the Kalman filter. " 1995.
  14. Nummiaro, K. , Esther K. -M. and Luc V. G. , "An adaptive color-based particle filters. " Image and Vision Computing, 2003.
  15. Chen, S. Y. "Kalman filter for robot vision: a survey. " IEEE Transactions on Industrial Electronics 2012.
  16. Bernardin, K. , Alexander E. and Rainer S. , "Multiple object tracking performance metrics and evaluation in a smart room environment. " Sixth IEEE International Workshop on Visual Surveillance, in conjunction with ECCV. Vol. 90. 2006.
  17. Ristic, B. , Sanjeev A. and Neil G. , "Beyond the Kalman filter: Particle filters for tracking applications. " Artech House Publishers, 2004.
  18. Lee, S. J. , et al. "Human tracking with an infrared camera using a curve matching framework. " EURASIP Journal on Advances in Signal Processing 2012.
  19. Deshpande andSuyog D. , et al. "Max-mean and max-median filters for detection of small targets. " SPIE's International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics, 1999.
  20. Loveridge and Jennifer C. "Adaptive, hybrid median filter for temporal noise suppression. " U. S. Patent No. 5,Jan. 1995.
  21. Li, R. , Bing Z. , and Ming L. L. , "Reliable motion detection/compensation for interlaced sequences and its applications to deinterlacing. " IEEE Transactions on Circuits and Systems for Video Technology, 2000.
  22. Silverman, J. , et al. "Temporal filtering for point target detection in staring IR imagery: II. Recursive variance filters. " Aerospace/Defense Sensing and Controls. International Society for Optics and Photonics, 1998.
  23. Lai, K. , et al. "A large-scale hierarchical multi-view rgb-d object dataset. " IEEE International Conference on Robotics and Automation (ICRA), 2011.
  24. Huang, S. -C. , "An advanced motion detection algorithm with video quality analysis for video surveillance systems. " IEEE Transactions on Circuits and Systems for Video Technology, 2011.
  25. Bae, T. -W. , "Small target detection using bilateral filter and temporal cross product in infrared images. " Infrared Physics & Technology, 2011.
  26. Kim, T. , Lee and Paik, "Combined shape and feature-based video analysis and its application to non-rigid object tracking. " IET Image Processing, 2011.
  27. Genin, L. , et al. "Point object detection using a NL-means type filter. " IEEE International Conference on Image Processing (ICIP), 2011.
  28. Neri, A. , et al. "Automatic moving object and background separation. " Signal Processing, 1998.
  29. Zheng Yi and Fan L. , "Moving object detection based on running average background and temporal difference," International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 270, 272, Nov. 2010.
  30. Sheikh, Y. , and Mubarak S. , "Bayesian object detection in dynamic scenes. " IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005
Index Terms

Computer Science
Information Sciences

Keywords

Infrared Imagery Target Tracking Algorithms Target Detection Algorithms Performance parameters