Learning pedestrian activities for semantic mapping

This paper proposes a semantic mapping method based on pedestrian activity in the urban road environment. Pedestrian activity patterns are learned from pedestrian tracks collected by a mobile platform. With the learned knowledge of pedestrian activity, semantic mapping is performed using Bayesian classification techniques. The proposed method is tested in real experiments, and shows promising results in recognizing four activity-related semantic properties of the urban road environment: pedestrian path, entrance/exit, pedestrian crossing and sidewalk.

[1]  Joachim Hertzberg,et al.  Towards semantic maps for mobile robots , 2008, Robotics Auton. Syst..

[2]  Gaurav S. Sukhatme,et al.  Semantic Mapping Using Mobile Robots , 2008, IEEE Transactions on Robotics.

[3]  Wolfram Burgard,et al.  Semantic labeling of places using information extracted from laser and vision sensor data , 2006 .

[4]  Eric Sommerlade,et al.  Modelling pedestrian trajectory patterns with Gaussian processes , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[5]  Sebastian Thrun,et al.  Learning Activity-Based Ground Models from a Moving Helicopter Platform , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Roland Siegwart,et al.  Bayesian space conceptualization and place classification for semantic maps in mobile robotics , 2008, Robotics Auton. Syst..

[7]  Philip H. S. Torr,et al.  Automatic dense visual semantic mapping from street-level imagery , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Paul Newman,et al.  Online generation of scene descriptions in urban environments , 2008, Robotics Auton. Syst..

[9]  Cipriano Galindo,et al.  Multi-hierarchical semantic maps for mobile robotics , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  W. Eric L. Grimson,et al.  Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models , 2011, International Journal of Computer Vision.

[11]  J. Laurie Snell,et al.  Markov Random Fields and Their Applications , 1980 .

[12]  Marcelo H. Ang,et al.  Metric mapping and topo-metric graph learning of urban road network , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[13]  E. Frazzoli,et al.  Autonomous personal vehicle for the first- and last-mile transportation services , 2011, 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS).

[14]  Emilio Frazzoli,et al.  Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment , 2013, 2013 IEEE International Conference on Robotics and Automation.

[15]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[16]  Wolfram Burgard,et al.  Supervised Learning of Places from Range Data using AdaBoost , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  Song-Chun Zhu,et al.  Inferring "Dark Matter" and "Dark Energy" from Videos , 2013, 2013 IEEE International Conference on Computer Vision.