Maier, Georg
[Author];
Reith-Braun, Marcel
[Author];
Bauer, Albert
[Author];
Gruna, Robin
[Author];
Pfaff, Florian
[Author];
Kruggel-Emden, Harald
[Author];
Längle, Thomas
[Author];
Hanebeck, Uwe D.
[Author];
Beyerer, Jürgen
[Author]
Machine learning based multiobject tracking for sensor based sorting
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Media type:
E-Article;
Electronic Conference Proceeding;
Text
Title:
Machine learning based multiobject tracking for sensor based sorting
Contributor:
Maier, Georg
[Author];
Reith-Braun, Marcel
[Author];
Bauer, Albert
[Author];
Gruna, Robin
[Author];
Pfaff, Florian
[Author];
Kruggel-Emden, Harald
[Author];
Längle, Thomas
[Author];
Hanebeck, Uwe D.
[Author];
Beyerer, Jürgen
[Author]
Footnote:
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Description:
Sensor-based sorting provides state-of-the-art solutions for sorting of granular materials. Current systems useline-scanning sensors, which yields a single observation of each object only and no information about their movement. Recent works show that using an area-scan camera bears the potential to decrease both the error in characterization and separation. Using a multiobject tracking system, this enables an estimate of the followed paths as well as the parametrization of an individual motion model per object. While previous works focus on physically-motivated motion models, it has been shown that state-of-the-art machine learning methods achieve an increased prediction accuracy. In this paper, we present the development of a neural network-based multiobject tracking system and its integration into a laboratory-scale sorting system. Preliminary results show that the novel system achieves results comparable to a highly optimized Kalman filter-based one. A benefit lies in avoiding tiresome manual tuning of parameters of the motion model, as the novel approach allows learning its parameters by provided examples due to its data-driven nature.