Fast Object Motion Estimation Based on Dynamic Stixels

The stixel world is a simplification of the world in which obstacles are represented as vertical instances, called stixels, standing on a surface assumed to be planar. In this paper, previous approaches for stixel tracking are extended using a two-level scheme. In the first level, stixels are tracked by matching them between frames using a bipartite graph in which edges represent a matching cost function. Then, stixels are clustered into sets representing objects in the environment. These objects are matched based on the number of stixels paired inside them. Furthermore, a faster, but less accurate approach is proposed in which only the second level is used. Several configurations of our method are compared to an existing state-of-the-art approach to show how our methodology outperforms it in several areas, including an improvement in the quality of the depth reconstruction.

[1]  Sergiu Nedevschi,et al.  Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters , 2015, IEEE Transactions on Intelligent Transportation Systems.

[2]  Cheng-Chew Lim,et al.  Histogram probabilistic multi-hypothesis tracker with colour attributes , 2015 .

[3]  J. Edmonds.  Paths, Trees, and Flowers , 1965, Canadian Journal of Mathematics.

[4]  Antonio Morell,et al.  MCL with sensor fusion based on a weighting mechanism versus a particle generation approach , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[5]  Jiaolong Xu,et al.  Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison , 2016, Sensors.

[6]  Reinhard Koch,et al.  A simple and efficient rectification method for general motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Leopoldo Acosta,et al.  Generating automatic road network definition files for unstructured areas using a multiclass support vector machine , 2016, Inf. Sci..

[9]  Luc Van Gool,et al.  Stixels estimation without depth map computation , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[10]  Rafael Arnay,et al.  Real-time adaptive obstacle detection based on an image database , 2011, Comput. Vis. Image Underst..

[11]  Uwe Franke,et al.  The Stixel World - A Compact Medium Level Representation of the 3D-World , 2009, DAGM-Symposium.

[12]  Luc Van Gool,et al.  Fast Stixel Computation for Fast Pedestrian Detection , 2012, ECCV Workshops.

[13]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  C. T. Ng,et al.  Measures of distance between probability distributions , 1989 .

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

[16]  Bodo Rosenhahn,et al.  Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[17]  Uwe Franke,et al.  Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-Time , 2010, ECCV.

[18]  Bodo Rosenhahn,et al.  Efficient Multiple People Tracking Using Minimum Cost Arborescences , 2014, GCPR.

[19]  Olivier D. Faugeras,et al.  The fundamental matrix: Theory, algorithms, and stability analysis , 2004, International Journal of Computer Vision.

[20]  Cheng Wang,et al.  Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[21]  Luc Van Gool,et al.  Robust Multiperson Tracking from a Mobile Platform , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Leopoldo Acosta,et al.  Path planning using a Multiclass Support Vector Machine , 2016, Appl. Soft Comput..

[23]  Dezhen Song,et al.  Featureless Motion Vector-Based Simultaneous Localization, Planar Surface Extraction, and Moving Obstacle Tracking , 2014, WAFR.

[24]  Sergiu Nedevschi,et al.  Particle Grid Tracking System Stereovision Based Obstacle Perception in Driving Environments , 2012, IEEE Intelligent Transportation Systems Magazine.

[25]  S. Savarese,et al.  Learning an Image-Based Motion Context for Multiple People Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Alberto Broggi,et al.  A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[27]  Rafael Arnay,et al.  Safe and reliable navigation in crowded unstructured pedestrian areas , 2016, Eng. Appl. Artif. Intell..

[28]  Timo Milbich,et al.  May I enter the roundabout? A time-to-contact computation based on stereo-vision , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[29]  Uwe Franke,et al.  Towards a Global Optimal Multi-Layer Stixel Representation of Dense 3D Data , 2011, BMVC.

[30]  Myoungho Sunwoo,et al.  Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles , 2012, IEEE Transactions on Intelligent Transportation Systems.

[31]  Luc Van Gool,et al.  Stixels Motion Estimation without Optical Flow Computation , 2012, ECCV.

[32]  Antonio Barrientos,et al.  Human Detection from a Mobile Robot Using Fusion of Laser and Vision Information , 2013, Sensors.

[33]  Dariu Gavrila,et al.  A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation , 2015, IEEE Transactions on Intelligent Transportation Systems.

[34]  Alexander Barth,et al.  Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision , 2009, IEEE Transactions on Intelligent Transportation Systems.

[35]  Uwe Franke,et al.  Efficient representation of traffic scenes by means of dynamic stixels , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[36]  Maziar Palhang,et al.  A region based CAMShift tracking with a moving camera , 2014, 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[37]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[38]  Stefan K. Gehrig,et al.  Exploiting the Power of Stereo Confidences , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  William D. Smart,et al.  Layered costmaps for context-sensitive navigation , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Andreas Zell,et al.  Robust and efficient volumetric occupancy mapping with an application to stereo vision , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[41]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[42]  Javier J. Sánchez Medina,et al.  Safe and Reliable Path Planning for the Autonomous Vehicle Verdino , 2016, IEEE Intelligent Transportation Systems Magazine.

[43]  C. G. Keller,et al.  Will the Pedestrian Cross? A Study on Pedestrian Path Prediction , 2014, IEEE Transactions on Intelligent Transportation Systems.

[44]  Stefan Roth,et al.  Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding , 2014, ECCV.

[45]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[46]  Miguel A. Olivares-Méndez,et al.  Vision-Based Steering Control, Speed Assistance and Localization for Inner-City Vehicles , 2016, Sensors.