Tracking of multiple people in crowds using laser range scanners

In everyday life, we can see amazing choreographies of movements of crowds of pedestrians. Pedestrians run into and avoid each other but do not seem to consciously cooperate. In this paper, we track a crowd of pedestrians in a large covered and cluttered area to understand their social behavior. Additionally, we try to analyze the characteristics of crowds of pedestrians such as traffic density, velocity, and trajectory. We introduce a stable feature extraction method based on accumulated distribution of successive laser frames. To isolate pedestrians, we propose a non-parametric method exploiting the Parzen windowing technique. We apply the new method of Rao-Blackwellized Monte Carlo data association to track a highly variable number of pedestrians. The algorithm is quantitatively evaluated through a social behavior experiment taking place in the lobby of a school. During this experiment, nearly 300 students are tracked.

[1]  T. McMahon,et al.  Ballistic walking. , 1980, Journal of biomechanics.

[2]  Joachim Denzler,et al.  SELF-CALIBRATION OF CAMERA NETWORKS: ACTIVE AND PASSIVE METHODS , 2011 .

[3]  Paolo Fiorini,et al.  Navigating a Robotic Wheelchair in a Railway Station during Rush Hour , 1999, Int. J. Robotics Res..

[4]  William Whittaker,et al.  Conditional particle filters for simultaneous mobile robot localization and people-tracking , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[5]  F. Bu,et al.  Pedestrian detection in transit bus application: sensing technologies and safety solutions , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[6]  Wolfram Burgard,et al.  Tracking multiple moving objects with a mobile robot , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  R. Shibasaki,et al.  A Robust Method for Registering Ground-Based Laser Range Images of Urban Outdoor Objects , 2001 .

[8]  Ryosuke Shibasaki,et al.  Detection and tracking of multiple pedestrians by using laser range scanners , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Juan I. Nieto,et al.  Multiple target tracking using Sequential Monte Carlo Methods and statistical data association , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[10]  M. Perc.  The dynamics of human gait , 2005 .

[11]  Ryosuke Shibasaki,et al.  Tracking multiple people using laser and vision , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[13]  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.

[14]  Dorin Comaniciu,et al.  Distribution Free Decomposition of Multivariate Data , 1998, Pattern Analysis & Applications.

[15]  Jouko Lampinen,et al.  Rao-Blackwellized Monte Carlo Data Association for Multiple Target Tracking , 2004 .

[16]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .