Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data
Author:
Navratil Gerhard1ORCID, Giannopoulos Ioannis1ORCID
Affiliation:
1. Department for Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria
Abstract
Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe aspects such as road surface conditions or traffic very careful. We thus performed an experiment to check whether IMU data is sufficient to classify motorcyclist behaviour as a data source for later spatial and temporal analysis. The classification was done using XGBoost and proved successful for four out of originally five different types of behaviour. A classification accuracy of approximately 80% was achieved. Only overtake manoeuvrers were not identified reliably.
Funder
Open Access Funding by TU Wien
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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