Abstract
Transportation plays a pivotal role in our society, especially in a good quality of life and economic prosperity. Intelligent transportation system (ITS) has been developed to manage the transport infrastructure and vehicles since the number of vehicles is rapidly growing and to avoid any accident. Various applications have provided to support ITS. One of them is a driver-assistant system. Considering of heavy vehicles such as bus, truck, trailer and etc., the driver assistant system is of importance in monitoring and recognizing objects in vehicle surrounding. For example, in operating a heavy vehicle, a driver has a limited view of the vehicle surrounding itself. It is difficult for the driver to ensure that the surrounding of vehicle is safe before operating the machine. Thus, in this paper, we employ a video tracking system through PSO and Parzen particle filter to break through several problems such as simultaneous motion and occlusion among objects. This method makes it easy to track a human movement from every frame and indirectly require less a processing time for tracking an object location in a video stream compared to conventional method. The detail outcome and result are discussed using experiments of the method in this paper.
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References
Broggi, A., Bertozzi, M., Fascioli, A., Guarino, C., Piazzi, A.: Visual perception of obstacles and vehicles for platooning. IEEE Trans. Intell. Transp. Syst., 164–176 (2000)
Chapuis, R., Aufrere, R., Chausse, F.: Accurate road following and reconstruction by computer vision. IEEE Trans. Intell. Transp. Syst., 261–270 (2002)
Kim, Y.U., Oh, S.Y.: Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving. IEEE Trans. Intell. Transp. Syst., 219–225 (2003)
Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection using evolutionary Gabor filter optimization. IEEE Trans. Intell. Transp. Syst., 125–137 (2005)
Kato, T., Ninomiya, Y., Masaki, I.: Preceding vehicle recognition based on learning from sample images. IEEE Trans. Intell. Transp. Syst., 57–68 (2002)
Escalera, A., Armingol, J.M., Pastor, J.M., Rodriguez, F.J.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Trans. Intell. Transp. Syst., 57–68 (2004)
Wu, W., Chen, X., Yang, J.: Detection of text on road signs from video. IEEE Trans. Intell. Transp. Syst., 378–390 (2005)
Li, S., Hayashi, A.: Navigation by integrating iconic and GPS information. In: Proc. IEEE Int. Conf. Intell. Veh., pp. 213–218 (1998)
Ye, Z., Liu, Z.-Q.: Tracking Human Hand Motion Using Genetic Particle Filter. In: IEEE International Conference on Systems, Man and Cybernetics (SMC 2006), pp. 4942–4947 (2006)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing (IEEE Transactions on Acoustics, Speech, and Signal Processing), 174–188 (2002)
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Musa, Z.B., Watada, J., Yan, S., Ding, H. (2009). Dynamic Tracking System through PSO and Parzen Particle Filter. In: Velásquez, J.D., RÃos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_28
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DOI: https://doi.org/10.1007/978-3-642-04592-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04591-2
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