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International Workshop on Activity Monitoring by Multiple Distributed Sensing

AMMDS 2014: Activity Monitoring by Multiple Distributed Sensing pp 52–63Cite as

TLD and Struck: A Feature Descriptors Comparative Study

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8703))

Abstract

Object tracking across multiple cameras is a very challenge issue in vision based monitoring applications. The selection of features is the first step to realize a reliable tracking algorithm.

In this work we analyse TLD and Struck, which are two of the most cited real-time visual trackers proposed in the literature in last years. They use two different feature extraction methodologies, Fern and Haar, respectively. The idea of this work is to compare performance of these well known visual tracking algorithms replacing their original feature characterization methods with local feature-based visual representations.

We test the improvement in terms of object detection and tracking performance grafting different features characterization into two completely different online tracker frameworks.

The used feature extraction methods are based on Local Binary Pattern (LBP), Local Gradient Pattern (LGP) and Histogram of Oriented Gradients (HOG). LGP is a novel detection methodology which is insensitive to global intensity variations like other representations such as local binary patterns (LBP).

The experimental results on well known benchmark sequences show as the feature extraction replacing improve the overall performances of the considered real-time visual trackers.

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Notes

  1. 1.

    http://www.cvg.rdg.ac.uk/PETS2009

  2. 2.

    http://www.bytefish.de/blog/local_binary_patterns/

  3. 3.

    http://www.vlfeat.org

  4. 4.

    For LGP source code: pierluigi.carcagni@ino.it.

  5. 5.

    http://www.samhare.net/research/struck

References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  2. Brehar, R., Nedevschi, S.: Local information statistics of LBP and HOG for pedestrian detection. In: 2013 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 117–122 (2013)

    Google Scholar 

  3. Chuan-xu, W., Zuo-yong, L.: A new face tracking algorithm based on local binary pattern and skin color information. In: International Symposium on Computer Science and Computational Technology 2008, ISCSCT ’08, vol. 2, pp. 657–660 (2008)

    Google Scholar 

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  5. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)

    Google Scholar 

  6. Dollar, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: Proceedings of BMVC, pp. 68.1–68.11 (2010). doi:10.5244/C.24.68

  7. D’Orazio, T., Leo, M., Mosca, N., Spagnolo, P., Mazzeo, P.L.: A semi-automatic system for ground truth generation of soccer video sequences. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance 2009, AVSS’09, pp. 559–564. IEEE (2009)

    Google Scholar 

  8. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  9. Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 263–270. IEEE (2011)

    Google Scholar 

  10. Hemery, B., Laurent, H., Rosenberger, C.: Comparative study of metrics for evaluation of object localisation by bounding boxes. In: Fourth International Conference on Image and Graphics 2007, ICIG 2007, pp. 459–464 (2007)

    Google Scholar 

  11. Jun, B., Choi, I., Kim, D.: Local transform features and hybridization for accurate face and human detection. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1423–1436 (2013)

    Article  Google Scholar 

  12. Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49–56. IEEE (2010)

    Google Scholar 

  13. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Machine Intell. 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  14. Khan, Z., Gu, I.H., Backhouse, A.: Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans. Circuits Syst. Video Technol. 21(1), 74–87 (2011)

    Article  Google Scholar 

  15. Kuo, C.-H., Huang, C., Nevatia, R.: Inter-camera association of multi-target tracks by on-line learned appearance affinity models. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 383–396. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. Mazzon, R., Cavallaro, A.: Multi-camera tracking using a multi-goal social force model. Neurocomputing 100, 41–50 (2013)

    Article  Google Scholar 

  17. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  18. Ozuysal, M., Fua, P., Lepetit, V.: Fast keypoint recognition in ten lines of code. In: IEEE Conference on Computer Vision and Pattern Recognition 2007, CVPR’07, pp. 1–8. IEEE (2007)

    Google Scholar 

  19. Rami, H., Hamri, M., Masmoudi, L.: Article: objects tracking in images sequence using local binary pattern (LBP). Int. J. Comput. Appl. 63(20), 19–23 (2013). (Published by Foundation of Computer Science, New York, USA)

    Google Scholar 

  20. Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)

    Article  Google Scholar 

  21. Shi, H., Lin, Z., Tang, W., Liao, B., Wang, J., Zheng, L.: A robust hand tracking approach based on modified tracking-learning-detection algorithm. In: Park, J.J.J.H., Chen, S.-C., Gil, J.-M., Yen, N.Y. (eds.) Multimedia and Ubiquitous Engineering. LNEE, vol. 308, pp. 9–15. Springer, Heidelberg (2014). http://dx.doi.org/10.1007/978-3-642-54900-7_2

    Chapter  Google Scholar 

  22. Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender classification based on boosting local binary pattern. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 194–201. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  23. Sun, S., Guo, Q., Dong, F., Lei, B.: On-line boosting based real-time tracking with efficient hog. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2297–2301 (2013)

    Google Scholar 

  24. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  25. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001, CVPR 2001, vol. 1, pp. I-511. IEEE (2001)

    Google Scholar 

  26. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418. IEEE (2013)

    Google Scholar 

  27. Xu, F., Gao, M.: Human detection and tracking based on hog and particle filter. In: 2010 3rd International Congress on Image and Signal Processing (CISP), vol. 3, pp. 1503–1507 (2010)

    Google Scholar 

  28. Yi, S., Yao, Z., Liu, J., Chen, J., Liu, W.: Robust tracking using on-line selection of multiple features. In: 2012 Spring Congress on Engineering and Technology (S-CET), pp. 1–5 (2012)

    Google Scholar 

  29. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)

    Article  Google Scholar 

  30. Zhang, L., Chu, R.F., Xiang, S., Liao, S.C., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Acknowledgment

This work has been supported by the “2007–2013 NOP for Research and Competitiveness for the Convergence Regions (Calabria, Campania, Puglia and Sicilia)” with code PON04a3_00201 and in part by the PON Baitah, with code PON01_980.

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Authors and Affiliations

  1. Faculty of Engineering, University of Salento, Lecce, Italy

    Francesco Adamo & Pierluigi Carcagnì

  2. National Research Council of Italy - Institute of Optics, Lecce, Italy

    Pier Luigi Mazzeo, Cosimo Distante & Paolo Spagnolo

Authors
  1. Francesco Adamo
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  2. Pierluigi Carcagnì
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  3. Pier Luigi Mazzeo
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  4. Cosimo Distante
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  5. Paolo Spagnolo
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Corresponding author

Correspondence to Francesco Adamo .

Editor information

Editors and Affiliations

  1. Istituto Nazionale di Ottica - CNR, Lecce, Italy

    Pier Luigi Mazzeo

  2. Istituto Nazioinale di Ottica - CNR, Lecce, Italy

    Paolo Spagnolo

  3. Aalborg University, Aalborg, Denmark

    Thomas B. Moeslund

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Adamo, F., Carcagnì, P., Mazzeo, P.L., Distante, C., Spagnolo, P. (2014). TLD and Struck: A Feature Descriptors Comparative Study. In: Mazzeo, P., Spagnolo, P., Moeslund, T. (eds) Activity Monitoring by Multiple Distributed Sensing. AMMDS 2014. Lecture Notes in Computer Science(), vol 8703. Springer, Cham. https://doi.org/10.1007/978-3-319-13323-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-13323-2_5

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  • Online ISBN: 978-3-319-13323-2

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