3-D model based vehicle recognition

We present a method for recognizing a vehicle's make and model in a video clip taken from an arbitrary viewpoint. This is an improvement over existing methods which require a front view. In addition, we present a Bayesian approach for establishing accurate correspondences in multiple view geometry. We take a model-based, top-down approach to classify vehicles. First, the vehicle pose is estimated in every frame by calculating its 3-D motion on a plane using a structure from motion algorithm. Then, exemplars from a database of 3-D models are rotated to the same pose as the vehicle in the video, and projected to the image. Features in the model images and the vehicle image are matched, and a model matching score is computed. The model with the best score is identified as the model of the vehicle in the video. Results on real video sequences are presented.

[1]  PollefeysMarc,et al.  Visual Modeling with a Hand-Held Camera , 2004 .

[2]  Timothy F. Cootes,et al.  Analysis of Features for Rigid Structure Vehicle Type Recognition , 2004, BMVC.

[3]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[4]  P. Anandan,et al.  Direct Recovery of Planar-Parallax from Multiple Frames , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Carsten Rother,et al.  Linear Multi View Reconstruction and Camera Recovery , 2001, ICCV.

[6]  Gérard G. Medioni,et al.  Motion pattern interpretation and detection for tracking moving vehicles in airborne video , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Dongjin Han,et al.  Vehicle Class Recognition from Video-Based on 3D Curve Probes , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[8]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[9]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[10]  Nelson H. C. Yung,et al.  Vehicle type classification from visual-based dimension estimation , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[11]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[12]  Geoffrey D. Sullivan,et al.  A Generic Deformable Model for Vehicle Recognition , 1995, BMVC.

[13]  Mubarak Shah,et al.  Floor Fields for Tracking in High Density Crowd Scenes , 2008, ECCV.

[14]  Rama Chellappa,et al.  A Factorization Method for Structure from Planar Motion , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[15]  Richard Szeliski,et al.  Vision Algorithms: Theory and Practice , 2002, Lecture Notes in Computer Science.

[16]  Geoffrey D. Sullivan,et al.  A Simple, Intuitive Camera Calibration Tool for Natural Images , 1994, BMVC.

[17]  Maurice Milgram,et al.  An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[18]  Anil K. Jain,et al.  Vehicle Segmentation and Classification Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[20]  Reinhard Koch,et al.  Visual Modeling with a Hand-Held Camera , 2004, International Journal of Computer Vision.

[21]  Qian Yu,et al.  Motion pattern interpretation and detection for tracking moving vehicles in airborne video , 2009, CVPR.