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Machine learning based pedestrian event monitoring using IMU and GPS
Mälardalens högskola, Akademin för innovation, design och teknik.
Mälardalens högskola, Akademin för innovation, design och teknik.
2018 (engelsk)Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
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%.

sted, utgiver, år, opplag, sider
2018. , s. 46
Emneord [en]
Machine Learning, Pedestrian, IMU, GPS, Hidden Markov Model, Artificial Neural Network, Event Monitoring
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-41100OAI: oai:DiVA.org:mdh-41100DiVA, id: diva2:1252213
Fag / kurs
Computer Science
Presentation
2018-09-21, Västerås, 15:05 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2018-10-19 Laget: 2018-10-01 Sist oppdatert: 2018-10-19bibliografisk kontrollert

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