Labeling Video Signal Obtained from an Event Camera

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Ghasemzadeh, Mehdi | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 55885 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Bagheri Shouraki, Saeed
Abstract:
Event cameras are bio-inspired sensors. They have outstanding properties compared to frame based cameras: high dynamic range (120 vs 60), low latency, no motion blur. Event cameras are appropriate for using in challenging scenarios such as vision system in self-driving cars and they have been used for high level machine vision tasks such as semantic segmentation, depth estimation. In this project, we worked on semantic segmentation using an event camera for self-driving cars. i) We introduce a large dataset, our dataset was produced using Carla simulator and it contains RGB images, events and semantic segmentation labels. ii) This project introduces new event based semantic segmentation model also we evaluate our model on Alonso dataset. iii) Event based models are robust but their accuracy is low compared to frame based models, we introduce a new event-frame based semantic segmentation model, our model uses RGB image and events and our experiment shows it is able to be robust and accurate
Keywords:
Machine Vision ; Automatic Vehicle Driving ; Self-Driving Car ; Semantic Segmentation ; Event Camera ; Event Based Vision ; Labeling Algorithm ; Video Signals

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