Jiangsu Yihe Technology Co.

Suzhou, China

Jiangsu Yihe Technology Co.

Suzhou, China
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Wu J.,Soochow University of China | Wu J.,Jiangsu Yihe Technology Co. | Cui Z.-M.,Soochow University of China | Cui Z.-M.,Jiangsu Yihe Technology Co. | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Video-based vehicle tracking movement in the field of intelligent transportation is an important research question. This paper presents a video vehicle tracking algorithm based on polar Fourier descriptor according to the Fourier transform properties. This paper first gets the ideal vehicle for the target object and extract its contour by using background subtraction for the reason that the traffic monitoring video scenes are fixed and then gets the video frequency spectrum characteristics of the vehicle by implementing Discrete Fourier Transform to the image data in polar coordinates and then achieves the video vehicle tracking based on linear prediction and shape similarity measurement. Experimental results show that this algorithm's tracking accuracy is higher and time-consuming is less. © 2011 Springer-Verlag.


Wu J.,Soochow University of China | Wu J.,Jiangsu Yihe Technology Co. | Cui Z.,Soochow University of China | Cui Z.,Jiangsu Yihe Technology Co. | And 3 more authors.
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2011

Vehicle tracking is a very important part of ITS and become a hot research field at home and abroad. In this paper, according to the multi-scales and multi-resolution analysis of Contourlet transform, put forward a video vehicle tracking algorithm based on the Contourlet transform. At first, according to the fixed characteristics of traffic video monitoring camera. we get the ideal vehicle target and the circum-rectangle with the method of reducing background, then, using Contourlet transform for the area of vehicle, we get the son of spectrum data in video vehicles. At last, realize video vehicle target tracking based on the vehicle image similarity measure and linear forecasting. Experiments show that this algorithm with higher tracking accuracy and better real-time. © 2011 IEEE.


Wu J.,Soochow University of China | Wu J.,Jiangsu Yihe Technology Co. | Cui Z.,Soochow University of China | Cui Z.,Jiangsu Yihe Technology Co. | And 4 more authors.
Journal of Computational Information Systems | Year: 2011

Motion understanding is the classification process for time-varying data, and vehicle motion understanding under road traffic scene is a systematic research work. This paper proposed a clustering-based vehicle motion understanding method, which preprocesses the obtained motion trajectory, then uses Fuzzy C Mean Clustering to cluster the preprocessed trajectory collection and uses Hausdorff Distance to classify vehicle trajectory to be tested. On the basis of correct classification, combined with the context information of vehicle object, vehicle behavior understanding is achieved. To verify the effectiveness of our method, we have experiments with violation-oriented traffic incident behavior. The experiment results show that our method has good feasibility and robustness. © 2005 by Binary Information Press.

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