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A novel pedestrian detection method based on Cost-Sensitive Support Vector Machine and Chaotic Particle Swarm Optimization with T mutation

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Warianty tytułu
PL
Nowa metoda detekcji pieszych bazująca na mechanizmie SVM i algorytmie mrówkowym z mutacją T
Języki publikacji
EN
Abstrakty
EN
This paper presents a novel pedestrian detection method based on chaotic particle swarm optimization with T mutation (CTPSO) and cost-sensitive support vector machine (CS-SVM). In order to solve the problem of class-imbalanced in pedestrian detection, a new improve SVM named CS-SVM is proposed, which is based on the idea of assigning different weights to the errors of the two classes when the numbers of data samples from each class are imbalanced. In addition, a new type of PSO called CTPSO is used to select suitable parameters of CS-SVM, which could improve the classification ability of CS-SVM prominently. CTPSO is a novel optimization algorithm, which not only has strong global search capability but also helps to find the optimum quickly by using chaos queues and T mutation. The experiment carried out on videos from INRIA, MIT and Daimler datasets, result indicates that the effectiveness and efficiency of the proposed method, which can achieve higher accuracy than other three state of the art algorithms.
PL
Przedstawiono nową metode detekcji pieszych bazującą na algorytmie mrówkowym z mutacją T oraz mechanizmie SVM. Zaproponowano nowy algorytm CS-SVM polegający na przyporządkowaniu różnych wag błędów w dwóch klasach kiedy liczba próbek w każdej klasie jest nierówna. Optimum znajdowane jest szybko przy wykorzystaniu mutacji T. Przeprowadzono eksperymenty bazujące na różnych bazach danych.
Rocznik
Strony
22--25
Opis fizyczny
Bibliogr. 15 poz., schem., wykr.
Twórcy
autor
autor
  • School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China, zhang_yang1983@163.com
Bibliografia
  • [1] M. Enzweiler, D. M. Gavrila. Monocular pedestrian detection: Survey and experiments. Journal of IEEE Trans. on Pattern Analysis and Machine Intelligence. 31(12):2179–2195, 2009.
  • [2] Ren Gang, Zhou Zhuping. Traffic safety forecasting method by particle swarm optimization and support vector machine. Expert Systems with Applications. 38(2011): 10420-10424,
  • [3] Zhao Chenglin, Sun Vuebin, Sun Songlin, Jiang Ting. Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine. Expert Systems with Applications. 38(2011): 9908-9912, 2011.
  • [4] Qi Wu, Rob Law. Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM. Expert Systems with Applications. 38(2011): 6405-6411,
  • [5] Cai Jiejin, Ma Xiaoqian, Li Lixiang, Peng Haipeng. Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Conversion and Management 48 (2007): 645–653, 2007.
  • [6] Yuchun Tang, Nitesh V. Chawla. SVMs Modeling for Highly Imbalanced Classification. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. 39(1): 281-288, 2009.
  • [7] Gyemin Lee, Clayton Scott. Nested Support Vector Machines. IEEE Trans. ON Signal Processing58(3): 1648-1660.
  • [8] S. Yan, S. Shan, X. Chen, and W. Gao. Locally Assembled Binary (LAB) feature with feature-centric cascade for fast and accurate face detection. In Proceedings of IEEE conference on Computer Vision and Pattern Recognition, pages 1–7, 2008.
  • [9] Xu Y.W., Cao X.B., Qiao H. An efficient tree classifier ensemble-based approach for pedestrian detection, J. of IEEE Trans. on Systems, Man, Cybernetics. 41(1):107-117, 2011.
  • [10] Rob W. W. Hooft, Leo H. Straver, Anthony L. Spek. Probability plots based on Student’s t-distribution. Journal of Acta Cryst. A65, 319-321, 2009.
  • [11] Liu Bo, Wang Ling, Jin Yi Hui, Tang Fang, Huang De Xian. Improved particle swarm optimization combined with chaos. Chaos, Solitons Fractals 25(5):1261–1271, 2005.
  • [12] Chan-Yun Yang, Jr-Syu Yang, Jian-Jun Wang. Margin calibration in SVM class-imbalanced learning. Neurocomputing. 73 (2009): 397–411, 2009.
  • [13] Gedikpinar, M. The speed control of DC motors with Support Vector Machine. Przeglad Elektrotechniczny. 87(5):269-271, 2011.
  • [14] Dems, M, lachecinski, S, Wiak, S. Influence of the constraints on the optimal problem solution using particle swarm algorithm. Przeglad Elektrotechniczny. 86(8):127-130, 2010.
  • [15] Luo, YF, Li JH, Huang P, Ji SC, Li YM. Chaotic characteristics of time series of partial discharges in oil-paper insulation and their applications in pattern recognition. Przeglad Elektrotechniczny. 87(7): 219-224, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOB-0049-0005
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