Trajectory annotation using sequences of spatial perception

• URL: http://arxiv.org/abs/2004.05383v1
• Date: Sat, 11 Apr 2020 12:22:27 GMT
• Title: Trajectory annotation using sequences of spatial perception
• Authors: Sebastian Feld (1), Steffen Illium (1), Andreas Sedlmeier (1), Lenz Belzner (2) ((1) Mobile and Distributed Systems Group LMU Munich, (2) MaibornWolff Munich)
• Abstract summary: In the near future, more and more machines will perform tasks in the vicinity of human spaces. This work builds a foundation to address this task. We propose an unsupervised learning approach based on a neural autoencoding that learns semantically meaningful continuous encodings of prototypical trajectory data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the verbal communication and the interaction between robotic units and/or humans, reliable and robust systems w.r.t. noise and processing results are needed. This work builds a foundation to address this task. By using a continuous representation of spatial perception in interiors learned from trajectory data, our approach clusters movement in dependency to its spatial context. We propose an unsupervised learning approach based on a neural autoencoding that learns semantically meaningful continuous encodings of spatio-temporal trajectory data. This learned encoding can be used to form prototypical representations. We present promising results that clear the path for future applications.

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