Reducing Processing Time for Histogram PMHT Algorithm in Video Object Tracking

Year 2018, Volume: 18 Issue: 2, 121 - 132, 03.08.2018

Ahmet Güngör Pakfiliz

Abstract

DOI:
10.26650/electrica.2018.36247

This
paper describes a novel approach for reducing the processing time of the
histogram probabilistic multi-hypothesis tracker (H-PMHT) algorithm in video
applications. Video data of flying vehicles is taken from surface to air, and a
temporal difference-based technique is applied to video frames for meeting the
intensity demands of H-PMHT algorithm. This technique also enables
discrimination between objects and eliminates clutter. Variations between the
structures of the standard and the improved version of H-PMHT algorithms are
described. In addition, the improved H-PMPT is compared with the standard
H-PMHT and another approved tracking algorithm to evaluate the performance and
processing time reduction ratings.

Keywords

Improved H-PMHT, Pixel Wise Difference, Surface to air, Video Tracking

Reducing Processing Time for Histogram PMHT Algorithm in Video Object Tracking

Abstract

DOI: 10.26650/electrica.2018.36247

This paper describes a novel approach for reducing the processing time of the histogram probabilistic multi-hypothesis tracker (H-PMHT) algorithm in video applications. Video data of flying vehicles is taken from surface to air, and a temporal difference-based technique is applied to video frames for meeting the intensity demands of H-PMHT algorithm. This technique also enables discrimination between objects and eliminates clutter. Variations between the structures of the standard and the improved version of H-PMHT algorithms are described. In addition, the improved H-PMPT is compared with the standard H-PMHT and another approved tracking algorithm to evaluate the performance and processing time reduction ratings.

Keywords

Improved H-PMHT, Pixel Wise Difference, Surface to air, Video Tracking

References

• 1. S. J. Davey, M. G. Rutten, B. Cheung, “A Comparison of Detection Performance for Several Track-before-Detect Algorithms”, EURASIP J. Adv. Signal Process, 2008. 2. C. Kathirvel, D. Mohanapriya, K. Mahesh, “A Novel Robust Approach for Moving Object Detection and Tracking in Video Surveillance System”, IJSRST, vol. 3, no. 8, pp.1235-1241, 2017. 3. X, Dong, J. Shen, D. Yu, W. Wang, J. Liu, H. Huang, “Occlusion-Aware Real-Time Object Tracking”, IEEE Transactions On Multimedia, vol. 19, no. pp. 763-771, 2017 4. S. S. Blackman, R. Popoli, “Design and Analysis of Modern Tracking Systems”, Artec House, Norwood, MA, USA, 1999. 5. Y. Bar-Shalom, X. R. Li, “Multitarget-Multisensor Tracking: Principles and Techniques”, YBS, Storrs, CT, USA, 1995. 6. R. L. Streit, “Tracking on Intensity-Modulated Data Streams, NUWC-NPT Technical Report 11.221, Naval Undersea Warfare Center Division” Newport, Rhode Island, USA, 2000. 7. H. X. Vu, S. J. Davey, “Track-Before-Detect using Histogram PMHT and Dynamic Programming, In Digital Image Computing Techniques and Applications”, International Conference (DICTA 2012), Fremantle, WA, 2012. 8. M. J. Walsh, M. L. Graham, R.L. Streit, T.E. Luginbuhl, L. E. Mathews, “Tracking on Intensity-Modulated Data Streams, Proceedings of the 2001”, IEEE Aerospace Conference, Big Sky, MT, 2001. 9. R. L. Streit, M.L. Graham, M. J. Walsh, “Tracking in Hyper-Spectral Data, Information Fusion 2002”, Annapolis, MD, USA, 2002, pp.852-859. 10. A. G. Pakfiliz, M. Efe, “Multi-Target Tracking in Clutter with Histogram Probabilistic Multi-Hypothesis Tracker, In Proc.” IEEE Conf Syst Eng, Las Vegas, USA, pp.137-142, 2005. 11. S. J. Davey, “Histogram PMHT with Particles”, In Proceedings of the 14th International Conference on Information Fusion”, Chicago, Illinois, USA, 2011. 12. M. Wieneke, S. J. Davey, “Histogram PMHT with Target Extent Estimates Based on Random Matrices”, In Proceedings of the 14th International Conference on Information Fusion, Chicago, Illinois, USA, July 2011. 13. Y. Bar-Shalom, T. Kirubarajan, X. Lin, “Probabilistic Data Association Techniques for Target Tracking with Applications to Sonar, Radar and EO Sensors”, IEEE Aerospace and Electronic Systems Magazine, vol. 20, no. 8, pp.37-56, 2005. 14. R. C. Gonzalez, R.E. Woods, “Digital Image Processing. 2nd ed. Prentice Hall, New Jersey (USA), 2002. 15. A.J. Lipton, H. Fujiyoshi, R. S. Patil, “Moving target Classification and Tracking from Real-Time Video”. Proc of Workshop Applications of Computer Vision, Princeton, New Jersey, USA, pp. 129-136, 1998. 16. C. Stauffer, W. Eric, L. Grimson, “Learning Patterns of Activity Using Real-Time Tracking”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, 2000. 17. Y. Boers, F. Ehlersand, W. Koch, T. Luginbuhland, L. D. Stone, R. L. Streit, “Track before Detect Algorithms”, EURASIP J Adv Signal Process, 2008. 18. S. J. Davey, H. X. Gaetjens, “Track-Before-Detect Using Expectation Maximisation: The Histogram Probabilistic Multi-hypothesis Tracker: Theory and Applications”, Springer, Singapore, 2018. 19. S. J. Davey, M. Wieneke, “Tracking Groups of People in Video with Histogram-PMHT” Defense Apps. of Signal Processing (DSAP) 2011 Workshop, Coolum, Queensland, Australia, 2011. 20. R. L. Streit, T. E. Luginbuhl, “Probabilistic Multi Hypothesis tracking, Technical Report NUWC-NPT 10,428, Naval Undersea Warfare Center Division”, Newport, Rhode Island, USA, 1995. 21. Y. Bar-Shalom, T.E. Fortmann, “Tracking and Data Association” Academic Press, NY, USA, 1988.