Detection and Tracking of Multiple Pedestrians in Automotive Applications

We present a method for tracking an unknown and changing number of far away pedestrians in a video stream. Multiple particle filter instances are utilized which track single pedestrians independently from each other. The tracking is guided by a cascade classifier which is integrated into the particle filter framework. In order to be able to detect hardly visible pedestrians and to filter out isolated false positives of the classifier, we developed a detection criterion for particle filters which follows the track-before-detect paradigm. The system nearly works in real time.

[1]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

[3]  William H. Press,et al.  Numerical recipes in C , 2002 .

[4]  Xia Liu,et al.  Pedestrian detection and tracking with night vision , 2005, IEEE Transactions on Intelligent Transportation Systems.

[5]  A. Shashua,et al.  Pedestrian detection for driving assistance systems: single-frame classification and system level performance , 2004, IEEE Intelligent Vehicles Symposium, 2004.

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

[7]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[8]  J. N. Driessen,et al.  Particle filter based detection for tracking , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Dariu Gavrila,et al.  A Bayesian Framework for Multi-cue 3D Object Tracking , 2004, ECCV.

[11]  Larry S. Davis,et al.  Pedestrian tracking from a moving vehicle , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[12]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[13]  Lawrence D. Stone,et al.  Bayesian Multiple Target Tracking , 1999 .

[14]  D. Paulus,et al.  Realtime Vision Based Multi-Target-Tracking with Particle Filters in Automotive Applications , 2006, 2006 IEEE Intelligent Vehicles Symposium.