Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties

Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation.

[1]  Liang Zhang,et al.  Multiple Vehicle-like Target Tracking Based on the Velodyne LiDAR , 2013 .

[2]  Dirk Haehnel,et al.  Junior: The Stanford entry in the Urban Challenge , 2008 .

[3]  VELODYNE’S HDL-64E: A HIGH DEFINITION LIDAR SENSOR FOR 3-D APPLICATIONS , 2009 .

[4]  Saeid Habibi,et al.  LiDAR Based Real Time Multiple Vehicle Detection and Tracking , 2016 .

[5]  Anthony F. Genovese.  The interacting multiple model algorithm for accurate state estimation of maneuvering targets , 2001 .

[6]  Stefan Roth,et al.  MOT16: A Benchmark for Multi-Object Tracking , 2016, ArXiv.

[7]  Luke Fletcher,et al.  A perception-driven autonomous urban vehicle , 2008 .

[8]  Ming-Hsuan Yang,et al.  UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking , 2015, Comput. Vis. Image Underst..

[9]  Herbert Freeman,et al.  Determining the minimum-area encasing rectangle for an arbitrary closed curve , 1975, CACM.

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

[11]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008 .

[12]  Konrad Schindler,et al.  Simultaneous Detection and Tracking of Pedestrian from Panoramic Laser Scanning Data , 2016 .

[13]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[14]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[15]  Markus Maurer,et al.  Multi-Target Tracking using a 3D-Lidar sensor for autonomous vehicles , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

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

[17]  A. Graziano,et al.  IMMJPDA versus MHT and Kalman filter with NN correlation: performance comparison , 1997 .

[18]  Markus Maurer,et al.  Stadtpilot: First fully autonomous test drives in urban traffic , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[19]  Jee-Hwan Ryu,et al.  Connected Components for a Fast and Robust 2D Lidar Data Segmentation , 2013, 2013 7th Asia Modelling Symposium.

[20]  Paolo Grisleri,et al.  The BRAiVE Autonomous Ground Vehicle Platform , 2010 .

[21]  Margrit Betke,et al.  Coupling detection and data association for multiple object tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[23]  Karsten Berns,et al.  Compact Data Association in Multiple Object Tracking: Pedestrian Tracking on Mobile Vehicle as Case Study , 2016 .

[24]  Volker Willert,et al.  Compact Representation of Dynamic Driving Environments for ADAS by Parametric Free Space and Dynamic Object Maps , 2016, IEEE Transactions on Intelligent Transportation Systems.

[25]  Paul Newman,et al.  Model-free detection and tracking of dynamic objects with 2D lidar , 2015, Int. J. Robotics Res..

[26]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

[27]  Y. Bar-Shalom,et al.  The probabilistic data association filter , 2009, IEEE Control Systems.

[28]  Zhenliang Ma,et al.  Improved IMM Algorithm for Nonlinear Maneuvering Target Tracking , 2012 .

[29]  Bin Dai,et al.  Likelihood-Field-Model-Based Vehicle Pose Estimation with Velodyne , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[30]  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.

[31]  Michael Himmelsbach,et al.  Fast segmentation of 3D point clouds for ground vehicles , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[32]  Markus Maurer,et al.  Toward Perception-Driven Urban Environment Modeling for Automated Road Vehicles , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[33]  Katalin György,et al.  Unscented Kalman Filters and Particle Filter Methods for Nonlinear State Estimation , 2014 .

[34]  Daoud Berkani,et al.  IMM-UKF algorithm and IMM-EKF algorithm for tracking highly maneuverable target: a comparison , 2005 .

[35]  Ulrich Hofmann,et al.  360 Degree multi sensor fusion for static and dynamic obstacles , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[36]  Bijun Li,et al.  Object detection and tracking using multi-layer laser for autonomous urban driving , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[37]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[38]  Michael Himmelsbach,et al.  Tracking and classification of arbitrary objects with bottom-up/top-down detection , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[39]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[40]  Matthias Schreier,et al.  Bayesian environment representation, prediction, and criticality assessment for driver assistance systems , 2017, Autom..