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Machine learning based pedestrian event monitoring using IMU and GPS
Mälardalen University, School of Innovation, Design and Engineering.
Mälardalen University, School of Innovation, Design and Engineering.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Understanding the behavior of pedestrians in road transportation is critical to maintain a safe en- vironment. Accidents on road transportation are one of the most common causes of death today. As autonomous vehicles start to become a standard in our society, safety on road transportation becomes increasingly important. Road transportation is a complex system with a lot of dierent factors. Identifying risky behaviors and preventing accidents from occurring requires better under- standing of the behaviors of the dierent persons involved. In this thesis the activities and behavior of a pedestrian is analyzed. Using sensor data from phones, eight dierent events of a pedestrian are classied using machine learning algorithms. Features extracted from phone sensors that can be used to model dierent pedestrian activities are identied. Current state of the art literature is researched to nd relevant machine learning algorithms for a classication model. Two models are implemented using two dierent machine learning algorithms: Articial Neural Network and Hid- den Markov Model. Two dierent experiments are conducted where phone sensor data is collected and classied using the models, achieving a classication accuracy of up to 93%.

Place, publisher, year, edition, pages
2018. , p. 46
Keywords [en]
Machine Learning, Pedestrian, IMU, GPS, Hidden Markov Model, Artificial Neural Network, Event Monitoring
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-41100OAI: oai:DiVA.org:mdh-41100DiVA, id: diva2:1252213
Subject / course
Computer Science
Presentation
2018-09-21, Västerås, 15:05 (English)
Supervisors
Examiners
Available from: 2018-10-19 Created: 2018-10-01 Last updated: 2018-10-19Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf