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Combining boosting machine learning and swarm intelligence for real time object detection and tracking: towards new meta-heuristics boosting classifiers

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Artificial vision in robotics involves real time detection of objects for fast decision making. Such intelligent systems require efficient algorithms and big learning database of examples for producing robust classifiers. Several methods of objects detection and tracking have been proposed in the literature. However, even though the detection rates have been improved, the processing time and the complexity of the models still representing a key challenge. In this paper, we present a real time object detection and tracking framework based on Adaboost classification, where a strong classifier is generated using an iterative combination of weak learners. This method is based on the use of discriminative features by analyzing different regions of the input image. Instead of performing a full traversal in the entire search space of all possible visual features, we propose to use intelligent heuristics for accelerating time processing and extracting relevant features in the image that lead to a best detection rate. The meta-heuristics involve the use of genetic algorithms, particle swarm optimization, random walk and a novel hybrid combination of these methods. The obtained results, in a case of intelligent transportation system, have shown considerable improvements in term of computation time, efficiency and accuracy.

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  • Abramson, Y., Freund, Y.: Semi-automatic visual learning (SEVILLE): tutorial on active learning for visual object recognition. In: Proc, CVPR (2005)

  • Abramson, Y., Moutarde, F., Stanciulescu, B., Steux, B.: Apprentissage de détecteurs visuels d’objets par dopage utilisant un algorithme hybride évolution-escalade. In: 8 conference francophone sur l’apprentissage automatique (CAp’2006), Tregastel, France (2006)

  • Abramson, Y., Steux, B., Ghorayeb, H.: Yet even faster (YEF) real-time object detection. IJISTA 2(2/3), 102–112 (2007)

    Article  Google Scholar 

  • Abril, P.S., Plant, R.: The patent holder’s dilemma: buy, sell, or troll? Commun. ACM 50(1), 36–44 (2007)

    Article  Google Scholar 

  • Andreopoulos, A., Tsotsos, J.K.: 50 years of object recognition: directions forward. Comput. Vis. Image Underst. 117(8), 827–891 (2013)

    Article  Google Scholar 

  • Antani, S., Kasturi, R., Jain, R.: A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognit. 35(4), 945–965 (2002)

    Article  MATH  Google Scholar 

  • Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer Inc, New York (2006)

    MATH  Google Scholar 

  • Bottou, L.: Stochastic Gradient Descent Tricks, pp. 421–436. Springer, Berlin (2012)

    Google Scholar 

  • Bradley, J.K., Schapire, R.E.: Filterboost: regression and classification on large datasets. In: Proceedings of the 20th international conference on neural information processing systems, NIPS’07, USA, pp. 185–192. Curran Associates Inc., New York (2007)

  • Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)

    Article  Google Scholar 

  • Collins, M., Schapire, R.E., Singer, Y.: Logistic regression, Adaboost and Bregman distances. Mach. Learn. 48(1–3), 253–285 (2002)

    Article  MATH  Google Scholar 

  • Conway, D., White, J.M.: Machine Learning for Hackers. O’Reilly Media, Sebastopol (2012)

    Google Scholar 

  • Ehrenfeucht, A., Haussler, D.: A general lower bound on the number of examples needed for learning. Inf. Comput. 82(3), 247–261 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  • Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)

    MathSciNet  MATH  Google Scholar 

  • Ghimire, D., Lee, J.: Geometric feature-based facial expression recognition in image sequences using multi-class Adaboost and support vector machines. Sensors 13(6), 7714–7734 (2013).

    Article  Google Scholar 

  • Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)

    Book  MATH  Google Scholar 

  • Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn., pp. 1–443. Addison-Wesley Longman Publishing Co., Inc., Boston (1989). ISBN:0201157675

    MATH  Google Scholar 

  • Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016).

  • Hajjem, M., Bouziri, H., Talbi, E.-G., Mellouli, K.: Parallel ant colony optimization for evacuation planning. In: Bosman, P.A.N. (ed.) Genetic and evolutionary computation conference, Berlin, Germany, July 15–19, 2017, Companion Material Proceedings, pp. 51–52. ACM (2017)

  • Ho, J., Yang, M.-H., Lim, J., Lee, K.-C.,Kriegman, D.: Clustering appearances of objects under varying illumination conditions. In: 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings, vol. 1, pp. I-11–I-18 (2003)

  • Kramer, O.: Genetic Algorithm Essentials, Volume 679 of Studies in Computational Intelligence. Springer, Berlin (2017)

    Book  Google Scholar 

  • Kennedy, J., Eberhart, R.C., Shi, Y. (eds.): Swarm Intelligence, The Morgan Kaufmann Series in Artificial Intelligence, pp. 475–495. Morgan Kaufmann, San Francisco (2001). ISBN:9781558605954

  • LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, CVPR’04, pp. 97–104, Washington, DC, USA. IEEE Computer Society (2004)

  • Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: proceedings of IEEE ICIP, pp. 10–14 (2002).

  • Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: DAGM-Symposium (2003)

  • Liu, H., Chen, S., Kubota, N.: Intelligent video systems and analytics: a survey. IEEE Trans. Ind. Inform. 9(3), 1222–1233 (2013)

    Article  Google Scholar 

  • Marsland, S.: Machine Learning: An Algorithmic Perspective, 1st edn. Chapman & Hall/CRC, London (2009)

    Google Scholar 

  • Mekami, H., Benabderrahmane, S., Bounoua, A., Taleb-Ahmed, A.: Local patterns and big time series data for facial poses classification. JCP 13(1), 18–34 (2018)

    Google Scholar 

  • Metidji, B., Taib, N., Baghli, L., Rekioua, T., Bacha, S.: Phase current reconstruction using a single current sensor of three-phase AC motors fed by SVM-controlled direct matrix converters. IEEE Trans. Ind. Electron. 60(12), 5497–5505 (2013)

    Article  Google Scholar 

  • Mitchell, T.M.: Machine Learning, 1st edn. McGraw-Hill Inc, New York (1997)

    MATH  Google Scholar 

  • Pai, G.A.V., Michel, T.: Metaheuristic optimization of constrained large portfolios using hybrid particle swarm optimization. Int. J. Appl. Metaheuristic Comput. 8(1), 1–23 (2017)

    Article  Google Scholar 

  • Panda, S., Padhy, N.P.: Comparison of particle swarm optimization and genetic algorithm for facts-based controller design. Appl. Soft Comput. 8(4), 1418–1427 (2008). (Soft Computing for Dynamic Data Mining)

    Article  Google Scholar 

  • Papageorgiou C., Oren, M., Poggio, T.A.: A general framework for object detection. In: Proceedings of 6th International Conference on Computer Vision, Bombay, 4–7 January 1998, pp. 555–562 (1998).

  • Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  • Palit, I., Reddy, C.K.: Scalable and parallel boosting with mapreduce. IEEE Trans. Knowl. Data Eng. 24(10), 1904–1916 (2012)

    Article  Google Scholar 

  • Rudin, C., Daubechies, I., Schapire, R.E.: The dynamics of adaboost: cyclic behavior and convergence of margins. J. Mach. Learn. Res. 5, 1557–1595 (2004)

    MathSciNet  MATH  Google Scholar 

  • Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the 16th international joint conference on artificial intelligence, vol 2, IJCAI’99, pp 1401–1406, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc, San Francisco (1999)

  • Schapire, R.E.: Theoretical views of boosting and applications. In: Proceedings of the 10th international conference on algorithmic learning theory, ALT ’99, London, UK, pp. 13–25. Springer, Berlin (1999)

  • Schapire, R.E.: Advances in boosting. In: Proceedings of the eighteenth conference on uncertainty in artificial intelligence, UAI’02, San Francisco, CA, USA, pp. 446–452. Morgan Kaufmann Publishers Inc., San Francisco (2002)

  • Schapire, R.E., Singer, Y.: Boostexter: a boosting-based systemfor text categorization. Mach. Learn. 39(2–3), 135–168 (2000)

    Article  MATH  Google Scholar 

  • Shantaiya, S., Verma, K., Mehta, K.: A survey on approaches of object detection. Int. J. Comput. Appl. 65(18), 14–20 (2013). (Published by Foundation of Computer Science, New York, USA)

    Google Scholar 

  • Talbi, E.-G.: Metaheuristics: from design to implementation. Wiley Publishing, Hoboken (2009)

    Book  MATH  Google Scholar 

  • Tavares, Y.M., Nedjah, N., de Macedo Mourelle, L.: Tracking patterns with particle swarm optimization and genetic algorithms. IJSIR 8(2), 34–49 (2017)

    Google Scholar 

  • Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)

    Article  MATH  Google Scholar 

  • Venter, G.: Review of Optimization Techniques. Wiley, New York (2010)

    Book  Google Scholar 

  • Viola, P.A., Jones, M.J.: Fast and robust classification using asymmetric Adaboost and a detector cascade. In: Proceedings of NIPS, pp. 1311–1318. MIT Press, Cambridge (2001a)

  • Viola, P.A., Jones, M.J.: 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, pp. 511–518 (2001b).

  • Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)

    Google Scholar 

  • Wu, Y., Chen, H., Zhao, X., Zhai, Y.: A vision-based recognition method for transformer based on adaboost and multi-template matching. In: 2015 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER), pp. 1429–1432 (2015)

  • Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)

    Article  Google Scholar 

  • Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 1–45 (2006).

    Article  Google Scholar 

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Correspondence to Sidahmed Benabderrahmane.

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This work was funded by Ecoles des Mines.

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Benabderrahmane, S. Combining boosting machine learning and swarm intelligence for real time object detection and tracking: towards new meta-heuristics boosting classifiers. Int J Intell Robot Appl 1, 410–428 (2017).

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