Correlation Technique for Estimating Traffic Speed from Cameras

A new algorithm is presented for estimating speed from roadside cameras in uncongested traffic, congested traffic, favorable weather conditions, and adverse weather conditions. Individual vehicle lanes are identified and horizontal vehicle features are emphasized by using a gradient operator. The features are projected into a one-dimensional subspace and transformed into a linear coordinate system by using a simple camera model. A correlation technique is used to summarize the movement of features through a group of images and estimate mean speed for each lane of vehicles.

[1]  Daniel J. Dailey,et al.  Dynamic camera calibration of roadside traffic management cameras , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[2]  Chunxiao Li,et al.  Acquisition of traffic information using a video camera with 2D spatio-temporal image transformation technique , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[3]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  An integrative method for video based traffic parameter extraction in ITS , 2000, IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on Circuits and Systems. Electronic Communication Systems. (Cat. No.00EX394).

[5]  Yongmin Kim,et al.  Video object tracking with a sequential hierarchy of template deformations , 2001, IEEE Trans. Circuits Syst. Video Technol..

[6]  S Bouzar,et al.  Traffic measurement: image processing using road markings , 1996 .

[7]  D. Levinson.  Detecting the Breakdown of Traffic Trb 2003 Annual Meeting Cd-rom Paper Revised from Original Submittal , 2002 .

[8]  I. Reading,et al.  Adaptive lane finding in road traffic image analysis , 1994 .

[9]  Jitendra Malik,et al.  A real-time computer vision system for measuring traffic parameters , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Satoshi Suzuki,et al.  Image velocity estimation from trajectory surface in spatiotemporal space , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Yasushi Yagi,et al.  Evaluating effectivity of map generation by tracking vertical edges in omnidirectional image sequence , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[12]  Bo Yang,et al.  A real-time vision system for automatic traffic monitoring based on 2D spatio-temporal images , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[13]  Mark S. Nixon,et al.  The velocity Hough transform: a new technique for dynamic feature extraction , 1997, Proceedings of International Conference on Image Processing.

[14]  N. H. C. Yung,et al.  Lane detection by orientation and length discrimination , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[15]  T. Nakamura,et al.  Methods of traffic flow measurement using spatio-temporal image , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).