Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Video-based situation assessment for road safety

Mohammad, Mahmud Abdulla 2016. Video-based situation assessment for road safety. PhD Thesis, Cardiff University.
Item availability restricted.

[img]
Preview
PDF - Accepted Post-Print Version
Download (22MB) | Preview
[img] PDF - Supplemental Material
Restricted to Repository staff only

Download (201kB)

Abstract

In recent decades, situational awareness (SA) has been a major research subject in connection with autonomous vehicles and intelligent transportation systems. Situational awareness concerns the safety of road users, including drivers, passengers, pedestrians and animals. Moreover, it holds key information regarding the nature of upcoming situations. In order to build robust automatic SA systems that sense the environment, a variety of sensors, such as global positioning systems, radars and cameras, have been used. However, due to the high cost, complex installation procedures and high computational load of automatic situational awareness systems, they are unlikely to become standard for vehicles in the near future. In this thesis, a novel video-based framework for the automatic assessment of risk of collision in a road scene is proposed. The framework uses as input the video from a monocular video camera only, avoiding the need for additional, and frequently expensive, sensors. The framework has two main parts: a novel ontology tool for the assessment of risk of collision, and semantic feature extraction based on computervision methods. The ontology tool is designed to represent the various relations between the most important risk factors, such as risk from object and road environmental risk. The semantic features related to these factors iii Abstract iv are based on computer vision methods, such as pedestrian detection and tracking, road-region detection and road-type classi�cation. The quality of these methods is important for achieving accurate results, especially with respect to video segmentation. This thesis, therefore, proposes a new criterion of high-quality video segmentation: the inclusion of temporal-region consistency. On the basis of the new criteria, an online method for the evaluation of video segmentation quality is proposed. This method is more consistent than the state-of-the-art method in terms of perceptual-segmentation quality, for both synthetic and real video datasets. Furthermore, using the Gaussian mixture model for video segmentation, one of the successful video segmentation methods in this area, new online methods for both road-type classi�cation and road-region detection are proposed. The proposed vision-based road-type classi�cation method achieves higher classi�cation accuracy than the state-of-the-art method, for each road type individually. Consequently, it achieves higher overall classi- �cation accuracy. Likewise, the proposed vision-based road-region detection method achieves high performance accuracy compared to the state-of-the-art methods, according to two measures: pixel-wise percentage accuracy and area under the receiver operating characteristic (ROC) curve (AUC). Finally, the evaluation performance of the automatic risk-assessment framework is measured. At this stage, the framework includes only the assessment of pedestrian risk in the road scene. Using the semantic information obtained via computer-vision methods, the framework's performance is assessed for two datasets: �rst, a new dataset proposed in Chapter 7, which comprises six videos, and second, a dataset comAbstract v prising �ve examples selected from an established, publicly available dataset. Both datasets consist of real-world videos illustrating pedestrian movement. The experimental results show that the proposed framework achieves high accuracy in the assessment of risk resulting from pedestrian behaviour in road scenes.

Item Type: Thesis (PhD)
Date Type: Publication
Status: Unpublished
Schools: Engineering
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Uncontrolled Keywords: Collision Risk Assessment; Ontology; Road Detection; Video Segmentation Evaluation; Road Scene Understanding; Online Video Segmentation.
Date of First Compliant Deposit: 18 October 2016
Last Modified: 28 Jun 2019 02:33
URI: http://orca.cf.ac.uk/id/eprint/94047

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics