Combining 3D Shape, Color, and Motion for Robust Anytime Tracking

Although object tracking has been studied for decades, real-time tracking algorithms often suffer from low accuracy and poor robustness when confronted with difficult, realworld data. We present a tracker that combines 3D shape, color (when available), and motion cues to accurately track moving objects in real-time. Our tracker allocates computational effort based on the shape of the posterior distribution. Starting with a coarse approximation to the posterior, the tracker successively refines this distribution, increasing in tracking accuracy over time. The tracker can thus be run for any amount of time, after which the current approximation to the posterior is returned. Even at a minimum runtime of 0.7 milliseconds, our method outperforms all of the baseline methods of similar speed by at least 10%. If our tracker is allowed to run for longer, the accuracy continues to improve, and it continues to outperform all baseline methods. Our tracker is thus anytime, allowing the speed or accuracy to be optimized based on the needs of the application.

[1]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[2]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[3]  Preben Alstrøm,et al.  Learning to Drive a Bicycle Using Reinforcement Learning and Shaping , 1998, ICML.

[4]  Tucker R. Balch,et al.  The multi‐iterative closest point tracker: An online algorithm for tracking multiple interacting targets , 2012, J. Field Robotics.

[5]  Huosheng Hu,et al.  3D mapping with multi-resolution occupied voxel lists , 2010, Auton. Robots.

[6]  Luke Fletcher,et al.  A perception‐driven autonomous urban vehicle , 2008, J. Field Robotics.

[7]  Sebastian Thrun,et al.  Precision tracking with sparse 3D and dense color 2D data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[8]  Andrew G. Barto,et al.  Autonomous shaping: knowledge transfer in reinforcement learning , 2006, ICML.

[9]  Sebastian Thrun,et al.  Model Based Vehicle Tracking for Autonomous Driving in Urban Environments , 2008, Robotics: Science and Systems.

[10]  Roland Siegwart,et al.  Generative object detection and tracking in 3D range data , 2012, 2012 IEEE International Conference on Robotics and Automation.

[11]  Vladimir Estivill-Castro,et al.  Hierarchical Monte-Carlo Localisation Balances Precision and Speed , 2004 .

[12]  Paul Newman,et al.  Self-calibration for a 3D laser , 2012, Int. J. Robotics Res..

[13]  Fabio M. Marchese,et al.  A robot localization method based on evidence accumulation and multi-resolution , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Hossein Mobahi,et al.  Seeing through the blur , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  K. Rose Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.

[16]  Nicolai Wojke,et al.  Moving vehicle detection and tracking in unstructured environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[17]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Edwin Olson,et al.  Real-time correlative scan matching , 2009, 2009 IEEE International Conference on Robotics and Automation.

[19]  Christoph Stiller,et al.  Joint self-localization and tracking of generic objects in 3D range data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[20]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[21]  Wolfram Burgard,et al.  Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[22]  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.

[23]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[24]  Olivier Aycard,et al.  Detection, classification and tracking of moving objects in a 3D environment , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[25]  Sebastian Thrun,et al.  Towards 3D object recognition via classification of arbitrary object tracks , 2011, 2011 IEEE International Conference on Robotics and Automation.

[26]  Clark F. Olson,et al.  Probabilistic self-localization for mobile robots , 2000, IEEE Trans. Robotics Autom..

[27]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[28]  Hans-Joachim Wünsche,et al.  Monocular model-based 3D vehicle tracking for autonomous vehicles in unstructured environment , 2011, 2011 IEEE International Conference on Robotics and Automation.

[29]  Peyman Milanfar,et al.  Modeling multiscale differential pixel statistics , 2006, Electronic Imaging.

[30]  C. Urmson,et al.  Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments , 2008, 2008 IEEE Intelligent Vehicles Symposium.