Improving multitarget tracking using orientation estimates for sorting bulk materials

Optical belt sorters can be used to sort a large variety of bulk materials. By the use of sophisticated algorithms, the performance of the complex machinery can be further improved. Recently, we have proposed an extension to industrial optical belt sorters that involves tracking the individual particles on the belt using an area scan camera. If the estimated behavior of the particles matches the true behavior, the reliability of the separation process can be improved. The approach relies on multitarget tracking using hard association decisions between the tracks and the measurements. In this paper, we propose to include the orientation in the assessment of the compatibility of a track and a measurement. This allows us to achieve more reliable associations, facilitating a higher accuracy of the tracking results.

[1]  Uwe D. Hanebeck,et al.  Simulation-based evaluation of predictive tracking for sorting bulk materials , 2016, 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[2]  Serge Reboul,et al.  A recursive fusion filter for angular data , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[3]  Uwe D. Hanebeck,et al.  Numerical modeling of an automated optical belt sorter using the Discrete Element Method , 2016 .

[4]  Jürgen Beyerer,et al.  TrackSort: Predictive tracking for sorting uncooperative bulk materials , 2015, 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[5]  Uwe D. Hanebeck,et al.  Association-free direct filtering of multi-target random finite sets with set distance measures , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[6]  P. Cundall,et al.  A discrete numerical model for granular assemblies , 1979 .

[7]  Uwe D. Hanebeck,et al.  Fast multitarget tracking via strategy switching for sensor-based sorting , 2016, 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[8]  Gerhard Kurz,et al.  Recursive estimation of orientation based on the Bingham distribution , 2013, Proceedings of the 16th International Conference on Information Fusion.

[9]  Oliver E. Drummond,et al.  Feature, attribute, and classification aided target tracking , 2001 .

[10]  A. Volgenant,et al.  A shortest augmenting path algorithm for dense and sparse linear assignment problems , 1987, Computing.

[11]  Hermann Wotruba,et al.  Stand der Technik der sensorgestützten Sortierung , 2008 .

[12]  Jürgen Beyerer,et al.  Real-time motion prediction using the chromatic offset of line scan cameras , 2017, Autom..

[13]  Gerhard Kurz,et al.  Multimodal circular filtering using Fourier series , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[14]  Gerhard Kurz,et al.  Discrete recursive Bayesian filtering on intervals and the unit circle , 2016, 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

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

[16]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

[17]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[18]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[19]  Gerhard Kurz,et al.  Recursive Bayesian filtering in circular state spaces , 2015, IEEE Aerospace and Electronic Systems Magazine.

[20]  U. Hanebeck,et al.  Improving material characterization in sensor-based sorting by utilizing motion information , 2017, Conference proceedings.

[21]  Siegmar Wirtz,et al.  Experimental and numerical investigation on the influence of particle shape and shape approximation on hopper discharge using the discrete element method , 2013 .

[22]  Uwe D. Hanebeck,et al.  Improving optical sorting of bulk materials using sophisticated motion models , 2016 .

[23]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .