Integrating Off-Board Cameras and Vehicle On-Board Localization for Pedestrian Safety

Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments, initially occluded or obscured dynamic objects can unpredictably enter the path of a vehicle. We propose a system that integrates a vision-based offboard pedestrian tracking subsystem with an onboard localization and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controller's field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. Alternatively, the system can be also applied to vehicles that have autonomous navigation capabilities, in which case, instead of alert lights, the vehicle is halted or redirected. We implemented and tested the proposed solution on an automated industrial vehicle under autonomous operation and on a human-driven vehicle in a full-scale production facility, over a period of four months.

[1]  Paulo Vinicius,et al.  Blob Motion Statistics for Pedestrian Detection , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[2]  Andrea Cavallaro,et al.  Multi-Camera Networks: Principles and Applications , 2009 .

[3]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Meng Wang,et al.  Automatic adaptation of a generic pedestrian detector to a specific traffic scene , 2011, CVPR 2011.

[5]  Ashley Tews,et al.  Real-Time Object Tracking and Classification Using a Static Came ra , 2009 .

[6]  Hugh F. Durrant-Whyte,et al.  Field and service applications - An autonomous straddle carrier for movement of shipping containers - From Research to Operational Autonomous Systems , 2007, IEEE Robotics & Automation Magazine.

[7]  Sharath Pankanti,et al.  Appearance models for occlusion handling , 2006, Image Vis. Comput..

[8]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.

[9]  Stefan Hörmann,et al.  Robot localization using 3D-models and an off-board monocular camera , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[10]  Katja Nummiaro A Color-based Particle Filter , 2002 .

[11]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[14]  Qi Tian,et al.  Foreground object detection from videos containing complex background , 2003, MULTIMEDIA '03.

[15]  C. Laurgeau,et al.  PUVAME - New French Approach for Vulnerable Road Users Safety , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[16]  Dariu Gavrila,et al.  Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle , 2007, International Journal of Computer Vision.

[17]  Jonathan M. Roberts,et al.  Autonomous Hot Metal Carrier , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

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

[19]  S. Johnsen,et al.  Real-Time Object Tracking and Classification Using a Static Camera , 2009 .

[20]  A. Fascioli,et al.  Pedestrian Protection Systems : Issues , Survey , and Challenges , 2007 .

[21]  Michael Bosse,et al.  Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM , 2008, Int. J. Robotics Res..

[22]  Larry S. Davis,et al.  Pedestrian Detection via Periodic Motion Analysis , 2007, International Journal of Computer Vision.