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Evaluation of Bounding Box Level Fusion of Single Target Video Object Trackers

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Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

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Abstract

The main objective of this work is to evaluate a simple fusion system which improves the performance of several object trackers, within a methodological and rigorous evaluation framework. The considered algorithms are monocamera single target trackers.

After analyzing in detail the state of the art, an evaluation framework is selected and presented. The sequences selected in this evaluation try to represent different real scenes and conditions. Then, clasical and modern tracking algorithms are selected and evaluated individually, in order to understand their performance in different scenarios and problems. Finally, some fusion methods are described and evaluated, comparing their results with the results of the individual tracking algorithms.

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References

  1. Maggio, E., Cavallaro, A.: Video Tracking: Theory and Practice. Wiley (2011)

    Google Scholar 

  2. Jalal, A.S., Singh, V.: The state-of-the-art in visual object tracking. Informatica 36(3), 227–248 (2012)

    Google Scholar 

  3. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 1–45 (2006)

    Article  Google Scholar 

  4. Heber, M., Godec, M., Rüther, M., Roth, P.M., Bischof, H.: Segmentation-based tracking by support fusion. Computer Vision and Image Understanding 117(6), 573–586 (2013)

    Article  Google Scholar 

  5. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. on Information Theory 21(1), 32–40 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  6. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)

    Article  Google Scholar 

  7. Nummiaro, K., Koller-Meier, E., Gool, L.V.: An adaptive colour-based particle filter. Image and Vision Computing 21(1), 99–110 (2002)

    Article  Google Scholar 

  8. Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56(3), 221–255 (2004)

    Article  Google Scholar 

  9. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision 77(1-3), 125–141 (2008)

    Article  Google Scholar 

  10. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2011)

    Article  Google Scholar 

  11. Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. IET Computer Vision 6(1), 62–69 (2012)

    Article  MathSciNet  Google Scholar 

  12. Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. IET Computer Vision 6(1), 52–61 (2012)

    Article  MathSciNet  Google Scholar 

  13. Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snásel, V.: Special issue: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)

    Article  Google Scholar 

  14. Abraham, A.: Special issue: Hybrid approaches for approximate reasoning. Journal of Intelligent and Fuzzy Systems 23(2), 41–42 (2012)

    MathSciNet  Google Scholar 

  15. Stenger, B., Woodley, T., Cipolla, R.: Learning to track with multiple observers. In: Proc of. Computer Vision and Pattern Recognition, pp. 2647–2654 (2009)

    Google Scholar 

  16. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)

    Google Scholar 

  17. Leichter, I., Lindenbaum, M., Rivlin, E.: A general framework for combining visual trackers, the black boxes approach. International Journal of Computer Vision 67, 343–363 (2006)

    Article  Google Scholar 

  18. Avidan, S.: Ensemble tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)

    Article  Google Scholar 

  19. Zhong, B., Yao, H., Chen, S., Ji, R., Yuan, X., Liu, S., Gao, W.: Visual tracking via weakly supervised learning from multiple imperfect oracles. In: Proc of. Computer Vision and Pattern Recognition, pp. 1323–1330 (2010)

    Google Scholar 

  20. Liu, W., Hauptmann, A.G.: A crowdsourcing approach to tracker fusion. Tech. rep., Carnegie Mellon University (2011)

    Google Scholar 

  21. McCane, B., Galvin, B., Novins, K.: Algorithmic fusion for more robust feature tracking. International Journal of Computer Vision 49(1), 79–89 (2002)

    Article  MATH  Google Scholar 

  22. Zhang, L., Gao, Y., Hauptmann, A., Ji, R., Ding, G., Super, B.: Symbiotic black-box tracker. In: Proc. of Advances in Multimedia Modeling, pp. 126–137 (2012)

    Google Scholar 

  23. Kwon, J., Lee, K.M.: Visual tracking decomposition. In: Proc. of Computer Vision and Pattern Recognition, pp. 1269–1276 (2010)

    Google Scholar 

  24. Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: Proc. of International Conference on Computer Vision, pp. 1195–1202 (2011)

    Google Scholar 

  25. Wang, H., Liu, C., Xu, L., Tang, M., Wu, X.: Multiple feature fusion for tracking of moving objects in video surveillance. Computational Intelligence and Security 1, 554–559 (2008)

    Google Scholar 

  26. Conaire, C.O., O’Connor, N.E., Smeaton, A.F.: Detector adaptation by maximising agreement between independent data sources. In: Proc. of Computer Vision and Pattern Recognition, pp. 1–6 (2007)

    Google Scholar 

  27. SanMiguel, J.C., Martinez, J.M.: Shadow detection in video surveillance by maximizing agreement between indep. detectors. In: Proc. of International Conference on Image Processing, pp. 1141–1144 (2009)

    Google Scholar 

  28. SPEVI, surveillance performance evaluation initiative, http://www.eecs.qmul.ac.uk/andrea/spevi.html

  29. Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(11), 1863–1868 (2006)

    Article  Google Scholar 

  30. PETS, “IEEE int. workshop perform. eval. trackking surveillance”

    Google Scholar 

  31. CAVIAR, Context aware vision using image-based active recognition, http://homepages.inf.ed.ac.uk/rbf/caviar/

  32. Vezzani, R., Cucchiara, R.: Video surveillance online repository (visor): an integrated framework. Multimedia Tools and Applications 50(2), 359–380 (2010)

    Article  Google Scholar 

  33. Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V., Zhang, J.: Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(2), 319–336 (2009)

    Article  Google Scholar 

  34. Nawaz, T., Cavallaro, A.: Pft: A protocol for evaluating video trackers. In: Proc. of International Conference on Image Processing, pp. 2325–2328 (2011)

    Google Scholar 

  35. Yin, F., Makris, D., Velastin, S.A.: Performance evaluation of object tracking algorithms. In: PETS, pp. 17–24 (2007)

    Google Scholar 

  36. Nawaz, T., Cavallaro, A.: A protocol for evaluating video trackers under real-world conditions. IEEE Trans. on Image Processing 22, 1354–1361 (2012)

    Article  MathSciNet  Google Scholar 

  37. Martín, R., Martínez, J.M.: Correlation study of video object trackers evaluation metrics. IET Electronics Letters 50(5), 361–363 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Video Processing and Understanding Lab, Universidad Autónoma de Madrid, Spain

    Rafael Martín & José M. Martínez

Authors
  1. Rafael Martín
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  2. José M. Martínez
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  2. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  3. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain and University of A Coruna, Escuela Universitaria Politecnica, Departamento de Enxeñeria Industrial, A Coruna, Spain

    Héctor Quintian

  6. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Martín, R., Martínez, J.M. (2014). Evaluation of Bounding Box Level Fusion of Single Target Video Object Trackers. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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