TY - GEN
T1 - Dynamic detection of transportation modes using Keypoint prediction
AU - Birth, Olga
AU - Frueh, Aaron
AU - Schlichter, Johann
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper proposes an approach that makes logical knowledge-based decisions, to determine the transportation mode a person is using in real-time. The focus is set to the detection of different public transportation modes. Hereby is analyzed how additional contextual information can be used to improve the decision making process. The methodology implemented is capable to differentiate between different modes of transportation including walking, driving by car, taking the bus, tram and (suburbain) trains. The implemented knowledge-based system is based on the idea of Keypoints, which provide contextual information about the environment. The proposed algorithm reached an accuracy of about 95%, which outclasses other methodologies in detecting the different public transportation modes a person is currently using.
AB - This paper proposes an approach that makes logical knowledge-based decisions, to determine the transportation mode a person is using in real-time. The focus is set to the detection of different public transportation modes. Hereby is analyzed how additional contextual information can be used to improve the decision making process. The methodology implemented is capable to differentiate between different modes of transportation including walking, driving by car, taking the bus, tram and (suburbain) trains. The implemented knowledge-based system is based on the idea of Keypoints, which provide contextual information about the environment. The proposed algorithm reached an accuracy of about 95%, which outclasses other methodologies in detecting the different public transportation modes a person is currently using.
KW - Context information
KW - Knowledge representation and acquisition
KW - Mobility and big data
KW - Public transport modes
KW - Real-time
UR - http://www.scopus.com/inward/record.url?scp=84955317560&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27926-8_5
DO - 10.1007/978-3-319-27926-8_5
M3 - Conference contribution
AN - SCOPUS:84955317560
SN - 9783319279251
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 59
BT - Machine Learning, Optimization, and Big Data - 1st International Workshop, MOD 2015 Taormina, Revised Selected Papers
A2 - Pavone, Mario
A2 - Farinella, Giovanni Maria
A2 - Cutello, Vincenzo
A2 - Pardalos, Panos
PB - Springer Verlag
T2 - 1st International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015
Y2 - 21 July 2015 through 23 July 2015
ER -