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ABSTRACT: Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases
the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems
in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this
paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents
an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into
coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected
from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy
and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive
to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint
lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and
the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy
test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of
objects in the real time video streams.
Key WordsObject Tracking-Optical Flow-Hough Transform-Voting
Journal of Mechanical Science and Technology 04/2012; 15(3):300-308. · 0.45 Impact Factor
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Proceedings of the 12th UKSim, International Conference on Computer Modelling and Simulation, Cambridge, UK, 24-26 March 2010; 01/2010
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Proceedings of the 12th UKSim, International Conference on Computer Modelling and Simulation, Cambridge, UK, 24-26 March 2010; 01/2010
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Proceedings of the Second International Joint Conference on Computational Sciences and Optimization, CSO 2009, Sanya, Hainan, China, 24-26 April 2009, Volume 1; 01/2009
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Proceedings of the International Conference on Ultra Modern Telecommunications, ICUMT 2009, 12-14 October 2009, St. Petersburg, Russia; 01/2009
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AMS 2009, Third Asia International Conference on Modelling & Simulation, 25-29 May 2009, Bandung, Bali, Indonesia; 01/2009
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Proceedings of the UKSim'11, International Conference on Computer Modelling and Simulation, Cambridge University, Emmanuel College, Cambridge, UK, 25-27 March 2009; 01/2009
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Int. J. Communication Systems. 01/2009; 22:1341-1354.
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IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2008, Sydney, NSW, Australia, December 10-12, 2008; 01/2008
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The Second International Conference on Future Generation Communication and Networking, FGCN 2008, Volume 2, Workshops, Hainan Island, China, December 13-15, 2008; 01/2008
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7th IEEE/ACIS International Conference on Computer and Information Science, IEEE/ACIS ICIS 2008, 14-16 May 2008, Portland, Oregon, USA; 01/2008
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Computational Science - ICCS 2008, 8th International Conference, Kraków, Poland, June 23-25, 2008, Proceedings, Part I; 01/2008
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Ubiquitous Intelligence and Computing, 4th International Conference, UIC 2007, Hong Kong, China, July 11-13, 2007, Proceedings; 01/2007
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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings; 01/2007
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Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings; 01/2007
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Technologies for E-Learning and Digital Entertainment, Second International Conference, Edutainment 2007, Hong Kong, China, June 11-13, 2007, Proceedings; 01/2007
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Image and Video Retrieval, 5th International Conference, CIVR 2006, Tempe, AZ, USA, July 13-15, 2006, Proceedings; 01/2006
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Advances in Knowledge Acquisition and Management, Pacific Rim Knowledge Acquisition Workshop, PKAW 2006, Guilin, China, August 7-8, 2006, Revised Selected Papers; 01/2006
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Fuzzy Systems and Knowledge Discovery, Third International Conference, FSKD 2006, Xi'an, China, September 24-28, 2006, Proceedings; 01/2006
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ABSTRACT: Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has
a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this
paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images
and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As
the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver
segmentation using histogram process has similar performance as the manual method by medical doctor.
07/2005: pages 421-421;