Confidence-based pedestrian tracking in unstructured environments using 3D laser distance measurements

Detection and tracking of pedestrians is an essential task for autonomous outdoor robots. Modern 3D laser range finders provide a rich and detailed 360 degree picture of the environment. Unstructured environments pose a difficult scenario where a variety of objects with similar shape to a human like shrubs or small trees occur. Especially in combination with partial occlusions, sensor noise, and conclusions from traversing rough terrain.

[1]  Kai Oliver Arras,et al.  Tracking people in 3D using a bottom-up top-down detector , 2011, 2011 IEEE International Conference on Robotics and Automation.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Christoph Mertz,et al.  Pedestrian Detection and Tracking Using Three-dimensional LADAR Data , 2010, Int. J. Robotics Res..

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[5]  Roland Siegwart,et al.  A Layered Approach to People Detection in 3D Range Data , 2010, AAAI.

[6]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[7]  Michael I. Jordan,et al.  Revisiting k-means: New Algorithms via Bayesian Nonparametrics , 2011, ICML.

[8]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  Cristiano Premebida,et al.  Exploiting LIDAR-based features on pedestrian detection in urban scenarios , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[11]  Thierry Chateau,et al.  Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner , 2010, IEEE Transactions on Intelligent Transportation Systems.

[12]  Ryo Kurazume,et al.  Multi-Part People Detection Using 2D Range Data , 2010, Int. J. Soc. Robotics.

[13]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[16]  Daniel D. Morris,et al.  Multi-sensor Detection and Tracking of Humans for Safe Operations with Unmanned Ground Vehicles , 2008 .

[17]  Dietrich Paulus,et al.  Probabilistic terrain classification in unstructured environments , 2013, Robotics Auton. Syst..

[18]  Armin B. Cremers,et al.  Person tracking in three-dimensional laser range data with explicit occlusion adaption , 2011, 2011 IEEE International Conference on Robotics and Automation.

[19]  Luc Van Gool,et al.  Cascaded Confidence Filtering for Improved Tracking-by-Detection , 2010, ECCV.

[20]  Masafumi Hashimoto,et al.  Multilayer lidar-based pedestrian tracking in urban environments , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[21]  Sebastian Thrun,et al.  Model based vehicle detection and tracking for autonomous urban driving , 2009, Auton. Robots.

[22]  Takashi Naito,et al.  Pedestrian recognition using high-definition LIDAR , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[23]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[24]  Shin'ichi Yuta,et al.  Fusion of double layered multiple laser range finders for people detection from a mobile robot , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.