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Human Motion Tracking via the Local Dissimilarity Map

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Abstract

In this paper, we propose a robust visual tracking algorithm based on local dissimilarity map (LDM) and Kalman filter (KF). Firstly, we model the motion model component of the proposed tracker by using the KF. Then, we apply the LDM into the object matching model to measure the local dissimilarities between the target and the sampled candidates in each frame of the given image sequence. Experimental results on several image sequences illustrate that the proposed method performs well in several challenging aspects of real world scenes.

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REFERENCES

  1. J.-F. Aujol, G. Gilboa, T. Chan, and S. Osher, “Structure-texture image decomposition—Modeling, algorithms, and parameter selection,” Int. J. Comput. Vision 67, 111–136 (2006). https://doi.org/10.1007/s11263-006-4331-z

    Article  MATH  Google Scholar 

  2. E. Baudrier, “Comparaison d’images binaires reposant sur une mesure locale des dissimilarités. Application à la classification,” PhD Thesis (Université de Reims Champagne-Ardenne, Reims, 2005).

  3. E. Baudrier, G. Millon, F. Nicolier, and S. Ruan, “Une méthode de comparaison d’images binaires quantifiant les dissimilarités locales Application à la classification d’impressions anciennes,” (2006), pp. 211–215.

  4. É. Baudrier, F. Nicolier, G. Millon, and S. Ruan, “Binary-image comparison with local-dissimilarity quantification,” Pattern Recognit. 41, 1461–1478 (2008). https://doi.org/10.1016/j.patcog.2007.07.011

    Article  MATH  Google Scholar 

  5. J. Black, T. Ellis, and P. Rosin, “Multi view image surveillance and tracking,” in Workshop on Motion and Video Computing, 2002, Orlando, Fla., 2002 (IEEE, 2002), pp. 169–174.  https://doi.org/10.1109/MOTION.2002.1182230

  6. Y. Ech-Choudany, “Analyse des signaux non-stationnaires à l’aide d’une nouvelle démarche de classification : application à l’identification de l’endommagement de matériaux composites par émission acoustique et à la détection de la crise d’épilepsie par EEG,” PhD Thesis (Université de Reims Champagne-Ardenne, Reims, 2018). http://www.theses.fr/2018REIMS020. Cited September 18, 2019.

  7. S. Edelman, “Representation is representation of similarities,” Behav. Brain Sci. 21, 449–467 (1998). https://doi.org/10.1017/S0140525X98001253

    Article  Google Scholar 

  8. G. Evangelopoulos and P. Maragos, “Image decomposition into structure and texture subcomponents with multifrequency modulation constraints,” in IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, Alaska, 2008 (IEEE, 2008), pp. 1–8. https://doi.org/10.1109/CVPR.2008.4587649

  9. J. Giebel, D. M. Gavrila, and C. Schnörr, “A Bayesian framework for multi-cue 3D object tracking,” in Computer Vision–ECCV 2004, Ed. by T. Pajdla and J. Matas, Lecture Notes in Computer Science, vol. 3024 (Springer, Berlin, 2004), pp. 241–252. https://doi.org/10.1007/978-3-540-24673-2_20

    Book  MATH  Google Scholar 

  10. J. Gilles, “Noisy image decomposition: A new structure, texture and noise model based on local adaptivity,” J. Math. Imaging Vision 28, 285–295 (2007). https://doi.org/10.1007/s10851-007-0020-y

    Article  MathSciNet  Google Scholar 

  11. S. Guo and X. Shi, “An improved spatiogram similarity measure for object tracking,” Pattern Recognit. Image Anal. 28, 79–86 (2018). https://doi.org/10.1134/S1054661818010169

    Article  Google Scholar 

  12. H. Jabnoun, F. Benzarti, F. Morain-Nicolier, and H. Amiri, “Video-based assistive aid for blind people using object recognition in dissimilar frames,” Int. J. Adv. Intell. Paradigms 12, 122–139 (2019). https://doi.org/10.1504/IJAIP.2019.10010766

    Article  Google Scholar 

  13. Y. Kim, B. Ham, M. N. Do, and K. Sohn, “Structure-texture image decomposition using deep variational priors,” IEEE Trans. Image Process. 28, 2692–2704 (2018). https://doi.org/10.1109/TIP.2018.2889531

    Article  MathSciNet  MATH  Google Scholar 

  14. N. Kumar and N. Sukavanam, “A cascaded CNN model for multiple human tracking and re-localization in complex video sequences with large displacement,” Multimedia Tools Appl. 79, 6109–6134 (2020). https://doi.org/10.1007/s11042-019-08501-4

    Article  Google Scholar 

  15. I. Ketata, F. Morain-Nicolier, L. Sallemi, S. Ruan, and A. B. Hamida, “Localisation de tumeurs dans des séquences TEP, par détection de changements au moyen de dissimilarités locales,” in CORESA 2013 – 16ème édition du colloque COmpression et REprésentation des Signaux Audiovisuels, Le Creusot, France, 2013, pp. 183–188.

  16. W. F. Leven and A. D. Lanterman, “Unscented Kalman filters for multiple target tracking with symmetric measurement equations,” IEEE Trans. Autom. Control 54, 370–375 (2009). https://doi.org/10.1109/TAC.2008.2008327

    Article  MathSciNet  MATH  Google Scholar 

  17. G. Mahfoudi, F. Morain-Nicolier, and F. Retraint, “Détection du copier-coller par mise en correspondance de descripteurs SIFT et filtrage à l’aide de cartes de dissimilarité locale,” p. 4.

  18. I. S. Molchanov and P. Terán, “Distance transforms for real-valued functions,” J. Math. Anal. Appl. 278, 472–484 (2003). https://doi.org/10.1016/S0022-247X(02)00719-9

    Article  MathSciNet  MATH  Google Scholar 

  19. F. Morain-Nicolier, J. Landré, and S. Ruan, “Détection d’objet par mesure de dissimilarités locales,” in XXIIe colloque GRETSI (traitement du signal & des images), Dijon, 2009, vol. 1, p. 92.

  20. F. Morain-Nicolier, J. Landré, and S. Ruan, “Gray level local dissimilarity map and global dissimilarity index for quality of medical images,” IFAC Proc. Vol. 42, 281–286 (2009). https://doi.org/10.3182/20090812-3-DK-2006.0073

  21. Vermaak, Doucet, and Perez, “Maintaining multimodality through mixture tracking,” in Proc. Ninth IEEE Int. Conf. on Computer Vision, Nice, 2003 (IEEE, 2003), vol. 2, pp. 1110–1116. https://doi.org/10.1109/ICCV.2003.1238473

  22. J. Wang, Y. Wang, C. Deng, and S. Wang, “Robust visual tracking based on convex hull with EMD-L1,” Pattern Recognit. Image Anal. 28, 44–52 (2018). https://doi.org/10.1134/S1054661818010078

    Article  Google Scholar 

  23. Y. Wang, “Robust visual tracking based on relaxed target representation,” Pattern Recognit. Image Anal. 29, 415–424 (2019). https://doi.org/10.1134/S1054661819030210

    Article  Google Scholar 

  24. E. Pekalska and R. P. W. Duin, Dissimilarity Representation for Pattern Recognition: Foundations and Applications, Series in Machine Perception and Artificial Intelligence, vol. 64 (World Scientific, 2005). https://doi.org/10.1142/5965

  25. Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” in IEEE Conf. on Computer Vision and Pattern Recognition, Portland, Ore., 2013 (IEEE, 2013), pp. 2411–2418. https://doi.org/10.1109/CVPR.2013.312

  26. K. Yang, J. Wang, Z. Shen, Z. Pan, and W. Yu, “Application of particle filter algorithm based on Gaussian clustering in dynamic target tracking,” Pattern Recognit. Image Anal. 29, 559–564 (2019). https://doi.org/10.1134/S1054661819030106

    Article  Google Scholar 

  27. Y. Zeng, X. Fu, L. Gao, J. Zhu, H. Li, and Y. Li, “Robust multivehicle tracking with Wasserstein association metric in surveillance videos,” IEEE Access 8, 47863–47876 (2020). https://doi.org/10.1109/ACCESS.2020.2978539

    Article  Google Scholar 

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Authors and Affiliations

  1. ISAC Laboratory, Department of Informatics, FSDM University of Sidi Mohamed Ben Abdellah, 30003, Fes, Morocco

    Wafae Mrabti & Hamid Tairi

  2. MCS Laboratory, National School of Applied Sciences First Mohammed University, 60000, Oujda, Morocco

    Benaissa Bellach

  3. COMS Laboratory, EA 7306, 57070, Metz, France

    Youssef Ech-Choudany

  4. CReSTIC, URCA-IUT de Troyes 9 rue de Québec CS 90396, 10026, TROYES Cedex, France

    Frédéric Morain-Nicolier

Authors
  1. Wafae Mrabti
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  2. Benaissa Bellach
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  3. Youssef Ech-Choudany
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  4. Frédéric Morain-Nicolier
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  5. Hamid Tairi
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Corresponding authors

Correspondence to Wafae Mrabti, Benaissa Bellach, Youssef Ech-Choudany, Frédéric Morain-Nicolier or Hamid Tairi.

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This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the PRIA Editorial Board decides not to accept it for publication.

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The authors declare that they have no conflicts of interest.

Additional information

Wafae Mrabti is a PhD student in image processing at Faculty of Sciences of Sidi Mohamed Ben Abdellah University. She is member in ISAC Laboratory. Her research interests include image processing, pattern recognition, and artificial intelligence.

Benaissa Bellach received his PhD in 2003 from Université de Bourgogne, France. He is a professor at the National high School of Applied Sciences (ENSA) at Mohammed First University, Oujda, Morocco. His research interests include image processing, pattern recognition, machine learning, 3D profiling by structured light projection techniques, and CMOS retina.

Youssef Ech-Choudany received the engineering degree in electronics and computer science from National School of Applied Sciences, Oujda, Morocco, in 2013, and the PhD degree in signal processing from Université de Reims Champagne-Ardenne (France) and First Mohammed University (Morocco), in 2018. His research interests include signal processing, machine learning, and computer vision, with application on biomedical and acoustic emission signals.

Frédéric Morain-Nicolier received his PhD in 2000 from Université de Bourgogne, France. In 2001 he joined the CReSTIC of Université de Reims-Champagne-Ardenne and became full professor in 2010. His research interests include medical image processing, historical studies, image forensics, CBIR, local and non-metric similarties, and perceptual similarities.

Hamid Tairi received his PhD degree in 2001 from the University Sidi Mohamed Ben Abdellah, Morocco. In 2002 he has done a postdoc in the Image Processing Group of the Laboratory LE2I (Laboratoire d’Electronique, Informatique et Image) in France. Since 2003, he has been an associate professor at the University Sidi Mohamed Ben Abdellah, Morocco. He is the director of the ISAC Laboratory. His research interests are in visual tracking for robotic control, in 3D reconstruction of artificial vision, in medical image, and in visual information retrieval and pattern recognition.

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Wafae Mrabti, Bellach, B., Ech-Choudany, Y. et al. Human Motion Tracking via the Local Dissimilarity Map. Pattern Recognit. Image Anal. 32, 162–173 (2022). https://doi.org/10.1134/S1054661822010047

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