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Knowledge Technology Week

KTW 2011: Knowledge Technology pp 143–153Cite as

Tracking Multiple Variable-Sizes Moving Objects in LFR Videos Using a Novel genetic Algorithm Approach

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 295))

Abstract

All available methods for tracking motion objects in videos have several challenges in many situations yet. For example, Particle filter methods cannot track objects that have variable sizes within frames duration efficiently. In this work, a novel multi-objective co-evolution genetic algorithm approach is developed that can efficiently track the variable size objects in low frame rate videos. We test our method on the famous PETS datasets in 10 categories with different frame rates and different number of motion objects in each scene. Our proposed method is a robust tracker against temporal resolution changes and it has better results in the tracking accuracy (about 10%) and lower false positive rate(about 7.5%) than classic particle filter and GA methods in the videos which contain variable size and small objects. Also it uses only 5 frames in each second instead of 15 or more frames.

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References

  1. Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Life Spans. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1728–1740 (2008)

    Article  Google Scholar 

  2. Konrad, J.: Motion detection and estimation. In: Image and Video Processing Handbook, ch. 3.8. Academic Press (2000)

    Google Scholar 

  3. Rodríguez, P.: Monitoring of Induced Motions in a Shake Table Test via Optical Motion Tracking: Preliminary Results. In: Ibero American Conference on Trends in Engineering Education and Collaboration, October 27 - 28 (2009)

    Google Scholar 

  4. Zhang, T., Fei, S., Lu, H., Li, X.: Modified Particle Filter for Object Tracking in Low Frame Rate Video. In: Proc. 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P.R. China, December 16-18 (2009)

    Google Scholar 

  5. Li, Z., Chen, J., Schraudolph, N.N.: An Improved Mean-Shift Tracker with Kernel Prediction and Scale Optimization Targeting for Low-Frame-Rate Video Tracking. In: Proceeding of the 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Florida, USA, December 8-11 (2008)

    Google Scholar 

  6. Rabiee, H.R., Asadi, M., Ghanbari, M.: Object tracking in crowded video scenes based on the undecimated wavelet features and texture analysis. EURASIP Journal on Advances in Signal Processing Archive 2008 (January 2008)

    Google Scholar 

  7. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Computer Vision 1(4), 321–331 (1987)

    Article  Google Scholar 

  8. Erdem, C.E., Tekalp, A.M., Sankur, B.: Video Object Tracking With Feedback of Performance Measures. IEEE Transaction for Circuit and Systems for Video Technology 13(4), 310–324 (2003)

    Article  Google Scholar 

  9. Chen, Y., Rui, Y., Huang, T.S.: JPDAF based HMM for real-time contour tracking. In: Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 543–550 (2001)

    Google Scholar 

  10. Gu, C., Lee, M.: Semiautomatic Segmentation and Tracking of Semantic Video Objects. IEEE Transaction for Circuit and Systems for Video Technology (5), 572–584 (1998)

    Google Scholar 

  11. Wang, D.: Unsupervised video segmentation based watersheds and temporal tracking. IEEE Transaction for Circuit and Systems for Video Technology 8(5), 539–546 (1998)

    Article  Google Scholar 

  12. Comaniciu, D., Ramesh, V., Meer, P.: Kernel Based Object tracking. IEEE Transactions Pattern Anal. Mach. Intell. 25(5), 546–577 (2003)

    Google Scholar 

  13. Karasulu, B.: Review and evaluation of well-known methods for moving object detection and tracking in videos. Journal of Aeronautics and Space Technologies 4(4), 11–22 (2010)

    Google Scholar 

  14. Carmona, E.J., Martínez-Cantos, J., Mira, J.: A new video segmentation method of moving objects based on blob-level knowledge. Pattern Recognition Letters 29(3), 272–285 (2008)

    Article  Google Scholar 

  15. Thirde, D., Borg, M., Aguilera, J., Wildenauer, H., Ferryman, J., Kampel, M.: Robust Real-Time Tracking for Visual Surveillance. EURASIP Journal on Advances in Signal Processing  2007, Article ID 96568, 23 pages (2007)

    Google Scholar 

  16. Okuma, K., Taleghani, A., de Freitas, N., Little, J.J., Lowe, D.G.: A Boosted Particle Filter: Multitarget Detection and Tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Liu, C., Shum, H.-Y., Zhang, C.: Hierarchical Shape Modeling for Automatic Face Localization. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 687–703. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Porikli, F., Tuzel, O.: Object Tracking in Low-Frame-Rate Video. In: SPIE Image and Video Comm. and Processing, vol. 5685, pp. 72–79 (2005)

    Google Scholar 

  19. Desai, U.B., Merchant, S.N., Zaveri, M., Ajishna, G., Purohit, M., Phanish, H.S.: Small object detection and tracking: Algorithm, analysis and application. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 108–117. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  20. Lu, H., Wang, D., Zhang, R., Chen, Y.-W.: Video object pursuit by tri-tracker with on-line learning from positive and negative candidates. IET Image Processing 5(1), 101–111 (2011)

    Article  Google Scholar 

  21. Abe, Y., Suzuki, H.: Multiple Targets Tracking Using Attention GA. In: Information Engineering and Computer Science, ICIECS 2009, International Conference on Computer Systems (2009)

    Google Scholar 

  22. Luque, R.M., Ortiz-de-Lazcano-Lobato, J.M., Lopez-Rubio, E., Palomo, E.J.: Object tracking in video sequences by unsupervised learning. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1070–1077. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  23. Ross, D., Lim, J., Lin, R., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1-3), 125–141 (2008)

    Article  Google Scholar 

  24. Gerónimo, D., López, A.M., Sappa, A.D., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(7), 1239–1258 (2010)

    Article  Google Scholar 

  25. DeJong, K.A., Spears, W.M.: An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 38–47. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  26. Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. Systems, Man, and Cybernetics SMC-16(1), 122–128 (1986)

    Article  Google Scholar 

  27. Mahfoud, S.W.: Niching methods for genetic algorithms. 251 pp (a dissertation from the University of Illinois) (also IlliGAL Report No. 95001) (1995)

    Google Scholar 

  28. PETS 2009 Dataset, http://www.cvg.rdg.ac.uk/PETS2009/index.html

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Author information

Authors and Affiliations

  1. Tarbiat Modares University, 111-14115, Tehran, Iran

    Hamidreza Shayegh Boroujeni, Nasrollah Moghadam Charkari, Mohammad Behrouzifar & Poonia Taheri Makhsoos

Authors
  1. Hamidreza Shayegh Boroujeni
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  2. Nasrollah Moghadam Charkari
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  3. Mohammad Behrouzifar
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  4. Poonia Taheri Makhsoos
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Editor information

Editors and Affiliations

  1. MIMOS Berhad, Knowledge Technology, Technology Park Malaysia, 57000, Kuala Lumpur, Malaysia

    Dickson Lukose

  2. Department of Systems and Network,College of Information Technology, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43000, Kajang, Malaysia

    Abdul Rahim Ahmad  & Azizah Suliman  & 

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Shayegh Boroujeni, H., Moghadam Charkari, N., Behrouzifar, M., Taheri Makhsoos, P. (2012). Tracking Multiple Variable-Sizes Moving Objects in LFR Videos Using a Novel genetic Algorithm Approach. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_15

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  • DOI: https://doi.org/10.1007/978-3-642-32826-8_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32825-1

  • Online ISBN: 978-3-642-32826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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