Predviđanje čovjekove trajektorije korištenjem metoda strojnog učenja

undergraduate thesis

• Title (english): Human trajectory prediction using machine learning methods
• Author: Robert Rastovski
• Mentor: Ivan Marković (mentor)
• Committee member: Ivan Marković (predsjednik povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Committee member: Anđela Jurić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-05, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Predviđanje trajektorije provedeno je korištenjem dva modela. Prvi model, model konstantne brzine, temelji se na nekoliko posljednjih položaja i srednjoj brzini bez algoritama strojnog učenja. Drugi model, model linearne regresije, temelji se na metodi strojnog učenja, a to je linearna regresija. Model linearne regresije koristi nekoliko položaja te računa brzinu koja minimizira grešku budućih položaja. Model se trenira korištenjem tih podataka te zatim koristi za predviđanje na neviđenim skupovima podataka. Rezultati oba modela su analizirani i uspoređeni.

Abstract (english)

Human trajectory prediction was implemented using two models. The first model, the constant velocity model, is based on the last few positions and average velocity without machine learning methods. The second model, the linear regression model, is based on the machine learning method and that method is linear regression. The linear regression model is using the last few positions and calculates velocity which minimizes the error of future positions. Model is trained using those data and then it is used for prediction on unseen data. Results of both models are analyzed and compared.

• Keywords: metode strojnog učenja; linearna regresija; srednja brzina; predviđanje položaja; python
• Keywords (english): machine learning methods; linear regression; average velocity; trajectory prediction; python
• Language: croatian
• URN:NBN: urn:nbn:hr:168:238576
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 21:32:15