Stochastic car tracking with line- and color-based features

Color- and edge-based trackers can often be "distracted", causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. It is also important for the tracker to maintain multiple hypotheses for the state; sequential Monte Carlo filters have been shown to be a convenient and straightforward means of maintaining multiple hypotheses. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with an edge-gradient-based shape feature under a sequential Monte Carlo framework.

[1]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  John Oliensis,et al.  A Multi-Frame Structure-from-Motion Algorithm under Perspective Projection , 1999, International Journal of Computer Vision.

[3]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[4]  Sun Ji-zhou.  Creating 3D Models with Uncalibrated Cameras , 2001 .

[5]  Frank Dellaert,et al.  Robust car tracking using Kalman filtering and Bayesian templates , 1998, Other Conferences.

[6]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[7]  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.

[8]  Daryl T. Lawton,et al.  Interactive Model-Based Vehicle Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

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

[11]  Bernt Schiele,et al.  Towards Robust Multi-cue Integration for Visual Tracking , 2001, ICVS.

[12]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[13]  David Nistér,et al.  Preemptive RANSAC for live structure and motion estimation , 2005, Machine Vision and Applications.

[14]  Jochen Triesch,et al.  Democratic Integration: Self-Organized Integration of Adaptive Cues , 2001, Neural Computation.

[15]  Rama Chellappa,et al.  A generic approach to simultaneous tracking and verification in video , 2002, IEEE Trans. Image Process..

[16]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  Thierry Pun,et al.  Combining Multiple Motion Estimates for Vehicle Tracking , 1996, ECCV.

[18]  Natan Peterfreund,et al.  Robust Tracking of Position and Velocity With Kalman Snakes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Hans-Hellmut Nagel,et al.  Analytical results on error sensitivity of motion estimation from two views , 1990, Image Vis. Comput..

[20]  Alexandru Tupan,et al.  Triangulation , 1997, Comput. Vis. Image Underst..

[21]  Ying Wu,et al.  A co-inference approach to robust visual tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[22]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[23]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

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

[25]  Berthold K. P. Horn.  Projective Geometry Considered Harmful , 2001 .

[26]  Jiang Li,et al.  Transductive inference for color-based particle filter tracking , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[27]  S. Shankar Sastry,et al.  Optimal segmentation of dynamic scenes from two perspective views , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[28]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[29]  Stephen M. Smith,et al.  ASSET-2: Real-Time Motion Segmentation and Shape Tracking , 1995, IEEE Trans. Pattern Anal. Mach. Intell..