Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking

Generating vehicle trajectories from video data is an important application of ITS (intelligent transportation systems). We introduce a new tracking approach which uses model-based 3-D vehicle detection and description algorithm. Our vehicle detection and description algorithm is based on a probabilistic line feature grouping, and it is faster (by up to an order of magnitude) and more flexible than previous image-based algorithms. We present the system implementation and the vehicle detection and tracking results.

[1]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[2]  Rama Chellappa,et al.  Higher order statistical learning for vehicle detection in images , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Ramakant Nevatia,et al.  Car detection in low resolution aerial image , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Hans-Hellmut Nagel,et al.  Model-based object tracking in monocular image sequences of road traffic scenes , 1993, International Journal of Computer 11263on.

[5]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  Ramakant Nevatia,et al.  Building Detection and Description from a Single Intensity Image , 1998, Comput. Vis. Image Underst..

[7]  Paul Smith,et al.  Effective Corner Matching , 1998, BMVC.

[8]  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.

[9]  Osama Masoud,et al.  Detection and classification of vehicles , 2002, IEEE Trans. Intell. Transp. Syst..

[10]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[11]  R. D. Ervin,et al.  System for Assessment of the Vehicle Motion Environment (SAVME): volume II , 2000 .

[12]  Geoffrey D. Sullivan,et al.  Pose refinement of active models using forces in 3D , 1994, ECCV.

[13]  Nathan H. Gartner,et al.  Traffic Flow Theory - A State-of-the-Art Report: Revised Monograph on Traffic Flow Theory , 2002 .

[14]  Takeo Kanade,et al.  A statistical approach to 3d object detection applied to faces and cars , 2000 .

[15]  A. Broggi,et al.  A cooperative approach to vision-based vehicle detection , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

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

[17]  L. A. Pipes An Operational Analysis of Traffic Dynamics , 1953 .