A Bayesian Approach: Fusion of Laser and Vision for Multiple Pedestrians Tracking

We present a promising system to simultaneously detect and track multiple pedestrians in the outside scene using laser and vision. The useful information of laser and vision is automatically extracted and combined in a Bayesian formulation. In order to compute MAP estimation, an effective Probabilistic Detection-based Particle Filter (PD-PF) has been proposed. Experiments and evaluations demonstrate that not only can our system perform robustly in real environments, but also obtain better approximation of MAP than previous methods in most complex situations.

[1]  Wolfram Burgard,et al.  Fast face detection for mobile robots by integrating laser range data with vision , 2003 .

[2]  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).

[3]  James J. Little,et al.  Robust Visual Tracking for Multiple Targets , 2006, ECCV.

[4]  Larry S. Davis,et al.  Fast multiple object tracking via a hierarchical particle filter , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Ming Yang,et al.  Intelligent Collaborative Tracking by Mining Auxiliary Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[6]  Stefan Carlsson,et al.  Multi-Target Tracking - Linking Identities using Bayesian Network Inference , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Xuan Song,et al.  Tracking interacting targets with laser scanner via on-line supervised learning , 2008, 2008 IEEE International Conference on Robotics and Automation.

[8]  Ajo Fod,et al.  Laser-Based People Tracking , 2002 .

[9]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[10]  Huosheng Hu,et al.  Vision and Laser Data Fusion for Tracking People with a Mobile Robot , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[11]  Xuan Song,et al.  Bayesian fusion of laser and vision for multiple People Detection and tracking , 2008, 2008 SICE Annual Conference.

[12]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  S. Godsill,et al.  Monte Carlo filtering for multi target tracking and data association , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[16]  Ryosuke Shibasaki,et al.  Fusion of Detection and Matching Based Approaches for Laser Based Multiple People Tracking , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Matthias Scheutz,et al.  Fast, reliable, adaptive, bimodal people tracking for indoor environments , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[18]  Mei Han,et al.  An algorithm for multiple object trajectory tracking , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[19]  L. Hong,et al.  Markov-chain Monte-Carlo approach for association probability evaluation , 2004 .

[20]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[21]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[22]  Larry S. Davis,et al.  Hierarchical Part-Template Matching for Human Detection and Segmentation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[23]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, CVPR.

[24]  Ryosuke Shibasaki,et al.  A novel system for tracking pedestrians using multiple single-row laser-range scanners , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Ray A. Jarvis,et al.  Panoramic Vision and Laser Range Finder Fusion for Multiple Person Tracking , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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