Context modelling for single and multi agent trajectory prediction
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Tharindu Warnakulasuriya Thesis
(PDF 69MB)
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This research addresses the problem of predicting future agent behaviour in both single and multi agent settings where multiple agents can enter and exit an environment, and the environment can change dynamically. Both short-term and long-term context was captured in the given domain and utilised neural memory networks to use the derived knowledge for the prediction task. The efficacy of the techniques was demonstrated by applying it to aircraft path prediction, passenger movement prediction in crowded railway stations, driverless car steering, predicting next shot location in tennis and for predicting soccer match outcomes.
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| ID Code: | 128480 | ||
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| Item Type: | QUT Thesis (PhD) | ||
| Supervisor: | Sridharan, Sridha & Fookes, Clinton | ||
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| Additional Information: | Recipient of the 2019 Outstanding Doctoral Thesis award | ||
| Keywords: | Context Modelling, Trajectory Prediction, Neural Memory Networks, Behaviour Modelling, Deep Fusion, Attention Mechanisms, Deep Learning, Computer Vision, Machine Learning, Artificial Intelligence, ODTA | ||
| DOI: | 10.5204/thesis.eprints.128480 | ||
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty | ||
| Institution: | Queensland University of Technology | ||
| Deposited On: | 29 May 2019 05:01 | ||
| Last Modified: | 09 Dec 2020 02:24 |
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