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Vehicle-Related Scene Understanding Using Deep Learning

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Thesis(2.4 MB)

Date

2019

Authors

Supervisor

Yan, Wei Qi

Item type

Thesis

Degree name

Master of Computer and Information Sciences

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

Abstract

Automated driving technology is an inevitable trend in the future development of transportation, it is also one of the eminent achievements in the matter of artificial intelligence. Primarily deep learning produces a significant contribution to the progression of automatic driving. Deep learning not only promotes autonomous vehicles to sense and identify the surrounding environment, but also identifies and classifies various information regarding to vehicles. With the upgrades and improvement of deep learning technology, it can be promptly and readily learned and employed. A large number of pretraining networks and public datasets have provided convenience for training numerous traffic scenes. Nevertheless, automated driving technology is not flexible enough to understand scenes in complex traffic environments, with regard to traffic rules and transportation facilities in various countries. There is no algorithm so far designed for all traffic scenes. In this thesis, our contributions are that we primarily deal with the issue of understanding of vehicle-related scene using deep learning. To the best of our knowledge, this is the first time that we utilize Auckland traffic environment as an analysis object for scene understanding. Moreover, automatic scene segmentation and object detection are coalesced for traffic scene understanding. The techniques based on deep learning dramatically decrease human manipulations. Furthermore, the datasets in this project provide a large amount of Auckland traffic data. Meanwhile, the performance of CNN processing is consolidated by combining with vehicle detection outcome.

Description

Keywords

Traffic scene understanding, Deep learning, Automatic driving, Image segmentation, Object detection

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Permanent link

https://hdl.handle.net/10292/12939

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Masters Theses