Leveraging spatio-temporal features to forecast time-to-accident
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Anjum, Taif
issued: 2023-07-12T00:06:07Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Globally, traffic accidents are the leading cause of death, according to reports published by the World Health Organization (WHO). Advanced Driver Assistance Systems (ADAS) effectively reduce the likelihood or severity of accidents. Time-To-Accident (TTA) forecasting is a critical component of ADAS; it can improve decision-making in traffic, dynamic path planning, and alert drivers to potential dangers. TTA is defined as the interval between the threat of an accident and the occurrence of the accident itself. Sensor or depth imaging-based TTA forecasting can be inaccurate and unaffordable to the mass. Existing vision-based traffic safety research in the literature focuses on accident anticipation with limited work on TTA prediction. We propose forecasting TTA by utilizing spatio-temporal features from dashboard camera videos and introducing a novel algorithm to compute ground truth TTA values. Additionally, we present two efficient architectures, a Light 3D Convolutional Neural Network (Li3D) and Hybrid CNN-Transformer (HyCT) network, for our application. Li3D uses significantly fewer parameters and larger kernels for efficient feature learning without sacrificing accuracy. We considered transfer learning to improve the model's performance further but faced limitations due to a lack of relevant large-scale video datasets and Li3D's input requirements. Thus we propose the HyCT network which is trained via transfer learning. HyCT outperformed a comprehensive list of networks, including Li3D on the Car Crash Dataset (CCD). Furthermore, with our approach, we can simultaneously forecast TTA and recognize accident and non-accident scenes. In real-world scenarios, it is vital to identify when a normal driving scene transitions into a potential accident scene. We assess our approach’s ongoing scene recognition ability using the TTAASiST@x (Time-to-Accident-based Accuracy at the xth frame Since Scene Transition) metric. With just 25% of the data, HyCT achieved over 85% accur…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85226
note: Leveraging Spatio-temporal Featuresto Forecast Time-To-AccidentbyTaif AnjumB.Sc. University of the Fraser Valley, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)July 2023© Taif Anjum, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Leveraging Spatio-temporal Features to Forecast Time-To-Accidentsubmitted by Taif Anjum in partial fulfilment of the requirements of thedegree of Master of ScienceDr. Apurva Narayan, Irving. K. Barber Faculty of ScienceSupervisorDr. John Braun, Irving. K. Barber Faculty of ScienceCo-SupervisorDr. Mohammad Khaled Hasan, Irving. K. Barber Faculty of ScienceSupervisory Committee MemberDr. Jeffrey Andrews, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Anas Chaaban, Faculty of Applied ScienceUniversity ExamineriiAbstractGlobally, traffic accidents are the leading cause of death, according to re-ports published by the World Health Organization (WHO). Advanced DriverAssistance Systems (ADAS) effectively reduce the likelihood or severity ofaccidents. Time-To-Accident (TTA) forecasting is a critical component ofADAS; it can improve decision-making in traffic, dynamic path planning,and alert drivers to potential dangers. TTA is defined as the interval betweenthe threat of an accident and the occurrence of the accident itself. Sensor ordepth imaging-based TTA forecasting can be inaccurate and unaffordableto the mass. Existing vision-based traffic safety research in the literaturefocuses on accident anticipation with limited work on TTA prediction. Wepropose forecasting TTA by utilizing spatio-temporal features from dash-board camera videos and introducing a novel algorithm to compute groundtruth TTA values.Additionally, we present two efficient architectures, a Light 3D Con-volut…
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0434193
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Narayan, Apurva
contributor: Braun, John, 1963-
type: Text
identifier URI: http://hdl.handle.net/2429/85226