Centralized fusion for fast people detection in dense environment

Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on CPU limited mobile architectures while remaining able to detect, track and classify objects as people with a very high precision. We present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications.

[1]  D. Streller,et al.  Vehicle and object models for robust tracking in traffic scenes using laser range images , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[2]  Huosheng Hu,et al.  Multisensor data fusion for joint people tracking and identification with a service robot , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

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

[4]  Bruno Steux,et al.  YEF (Yet Even Faster) Real-Time Object Detection , 2005, ALaRT.

[5]  G. Gate,et al.  Using targets appearance to improve pedestrian classification with a laser scanner , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[6]  Klaus Dietmayer,et al.  Pedestrian recognition in urban traffic using a vehicle based multilayer laserscanner , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[7]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Fabien Moutarde,et al.  Title-Template for Papers ECOC 2004 , 2007 .

[9]  Bruno Steux,et al.  Yet Even Faster (YEF) real-time object detection , 2007, Int. J. Intell. Syst. Technol. Appl..

[10]  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.

[11]  Cristiano Premebida,et al.  A Multi-Target Tracking and GMM-Classifier for Intelligent Vehicles , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[12]  Bruno Steux,et al.  YEF∗Real-Time Object Detection , 2004 .

[13]  Ryosuke Shibasaki,et al.  Joint Tracking and Classification of Moving Objects at Intersection Using a Single-Row Laser Range Scanner , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[14]  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.

[15]  V. Cherfaoui,et al.  Tracking objects using a laser scanner in driving situation based on modeling target shape , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[16]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[17]  Grzegorz Cielniak,et al.  Person identification by mobile robots in indoor environments , 2003, 1st International Workshop on Robotic Sensing, 2003. ROSE' 03..

[18]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[19]  L.C. Bento,et al.  Multi-target detection and tracking with a laser scanner , 2004, IEEE Intelligent Vehicles Symposium, 2004.