A fuzzy prediction based trajectory estimation

Image processing, object tracking and trajectory estimation are the integral topics of computer vision which are rapidly gaining importance due to their non-ignorable relation with defense, security and also health sectors. In this paper; by use of image processing techniques, real time images containing tracked object from the camera are transferred to a Visual Basic based software; tracked object in images is discerned from the background and using segmentation process transferred into matrix form via which object centre point coordinates are determined. After determination of centre point coordinates, the trajectory the moving object is estimated with an adaptive fuzzy time series forecasting model using the determined coordinate values.

[1]  Sheng-Tun Li,et al.  A hidden Markov model-based forecasting model for fuzzy time series , 2006 .

[2]  Ernesto Araujo,et al.  Video Target Tracking by Using Competitive Neural Networks , 2022 .

[3]  Dobrivoje Popovic,et al.  Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control) , 2005 .

[4]  HAKAN TOZAN,et al.  Fuzzy forecasting applications on supply chains , 2008 .

[5]  Fredrik Gustafsson,et al.  Ground Target Recognition Using Rectangle Estimation , 2006, IEEE Transactions on Image Processing.

[6]  Hakan Tozan,et al.  The effects of fuzzy forecasting models on supply chain performance , 2008 .

[7]  Grace F. Olsen,et al.  Fundamental work toward an image processing-empowered dental intelligent educational system , 2010 .

[8]  Shyi-Ming Chen,et al.  Handling forecasting problems using fuzzy time series , 1998, Fuzzy Sets Syst..

[9]  Steven Nahmias,et al.  Production and operations analysis , 1992 .

[10]  Claudio Guarnaccia.  Acoustical noise analysis in road intersections: a case study , 2010 .

[11]  Tzuu-Hseng S. Li,et al.  Fuzzy target tracking control of autonomous mobile robots by using infrared sensors , 2004, IEEE Transactions on Fuzzy Systems.

[12]  Masatoshi Ishikawa,et al.  Moment feature-based three-dimensional tracking using two high-speed vision systems , 2003, Adv. Robotics.

[13]  Hakan Tozan,et al.  A hybrid Grey & ANFIS approach to bullwhip effect in supply chain networks , 2009 .

[14]  M. Kemal Ciliz,et al.  Combined direct and indirect adaptive control of robot manipulators using multiple models , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..

[15]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[16]  Cengiz Kahraman.  Fuzzy Applications in Industrial Engineering (Studies in Fuzziness and Soft Computing) , 2006 .

[17]  Qiang Song,et al.  A new fuzzy time-series model of fuzzy number observations , 1995 .

[18]  Vasile Gui,et al.  Fast and accurate background subtraction for video surveillance, using an adaptive mode -tracking algorithm , 2005 .

[19]  B. Chissom,et al.  Forecasting enrollments with fuzzy time series—part II , 1993 .

[20]  Steven Nahmias,et al.  Production and operations analysis -4/E , 2005 .

[21]  Mustafa Yagimli,et al.  A GPS-based system design for the recognition and tracking of moving targets , 2009, 2009 4th International Conference on Recent Advances in Space Technologies.

[22]  B. Chissom,et al.  Fuzzy time series and its models , 1993 .

[23]  A. N. JHA.  A FINE LOAD FORECASTING USING NEURAL NETWORKS AND FUZZY NEURAL NETWORKS , 2005 .

[24]  Hsuan-Shih Lee,et al.  Fuzzy forecasting based on fuzzy time series , 2004, Int. J. Comput. Math..

[25]  Chaohui Wang,et al.  Predicting tourism demand using fuzzy time series and hybrid grey theory. , 2004 .