In recent years, Intelligent Transportation Systems (ITS) have emerged as

an efficient way of enhancing traffic flow, safety and management. These

goals are realized by combining various technologies and analyzing the acquired

data from vehicles and roadways. Among all ITS technologies, computer

vision solutions have the advantages of high flexibility, easy maintenance

and high price-performance ratio that make them very popular for

transportation surveillance systems. However, computer vision solutions

are demanding and challenging due to computational complexity, reliability,

efficiency and accuracy among other aspects.

In this thesis, three transportation surveillance systems based on computer

vision are presented. These systems are able to interpret the image

data and extract the information about the presence, speed and class of

vehicles, respectively. The image data in these proposed systems are acquired

using Unmanned Aerial Vehicle (UAV) as a non-stationary source

and roadside camera as a stationary source. The goal of these works is to

enhance the general performance of accuracy and robustness of the systems

with variant illumination and traffic conditions.

This is a compilation thesis in systems engineering consisting of three

parts. The red thread through each part is a transportation surveillance

system. The first part presents a change detection system using aerial images

of a cargo port. The extracted information shows how the space is

utilized at various times aiming for further management and development

of the port. The proposed solution can be used at different viewpoints and

illumination levels e.g. at sunset. The method is able to transform the images

taken from different viewpoints and match them together. Thereafter,

it detects discrepancies between the images using a proposed adaptive local

threshold. In the second part, a video-based vehicle's speed estimation

system is presented. The measured speeds are essential information for law

enforcement and they also provide an estimation of traffic flow at certain

points on the road. The system employs several intrusion lines to extract

the movement pattern of each vehicle (non-equidistant sampling) as an input

feature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are also

taken into account to address the uncertainty in the measurements and to

obtain the probability density function of the vehicle's speed. In the third

part, a vehicle classification system is provided to categorize vehicles into

\private car", \light trailer", \lorry or bus" and \heavy trailer". This information

can be used by authorities for surveillance and development of

the roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. The

system has been constructed by using prior knowledge of traffic regulations

regarding each class of vehicle in order to enhance the classification performance.

Computer vision solutions play a significant role in intelligent transportation systems (ITS) by improving traffic flow, safety and management. In addition, they feature prominently in autonomous vehicles and their future development. The main advantages of vision-based systems are their flexibility, coverage and accessibility. Moreover, computational power and recent algorithmic advances have increased the promise of computer vision solutions and broadened their implementation. However, computational complexity, reliability and efficiency remain among the challenges facing vision-based systems.

Most traffic surveillance systems in ITS comprise three major criteria: vehicle detection, tracking and classification. In this thesis, computer vision systems are introduced to accomplish goals corresponding to these three criteria: 1) to detect the changed regions of an industrial harbour's parking lot using aerial images, 2) to estimate the speed of the vehicles on the road using a stationary roadside camera and 3) to classify vehicles using a stationary roadside camera and aerial images.

The first part of this thesis discusses change detection in aerial images, which is the core of many remote sensing applications. The aerial images were taken over an industrial harbour using unmanned aerial vehicles on different days and under various circumstances. This thesis presents two approaches to detecting changed regions: a local pattern descriptor and three-dimensional feature maps. These methods are robust to varying illumination and shadows. Later, the introduced 3D feature map generation model was employed for vehicle detection in aerial images.

The second part of this thesis deals with vehicle speed estimation using roadside cameras. Information regarding the flow, speed and number of vehicles is essential for traffic surveillance systems. In this thesis, two vision-based vehicle speed estimation approaches are proposed. These analytical models consider the measurement uncertainties related to the camera sampling time. The main contribution of these models is to estimate a speed probability density function for every vehicle. Later, the speed estimation model was utilised for vehicle classification using a roadside camera.

Finally, in the third part, two vehicle classification models are proposed for roadside and aerial images. The first model utilises the proposed speed estimation method to extract the speed of the passing vehicles. Then, we used a fuzzy c-means algorithm to classify vehicles using their speeds and dimension features. The results show that vehicle speed is a useful feature for distinguishing different categories of vehicles. The second model employs deep neural networks to detect and classify heavy vehicles in aerial images. In addition, the proposed 3D feature generation model was utilised to improve the performance of the deep neural network. The experimental results show that 3D feature information can significantly reduce false positives in the deep learning model's output.

This thesis comprises two chapters: Introduction, and Publications. In the introduction section, we discuss the motivation for computer vision solutions and their importance. Furthermore, the concepts and algorithms used to construct the proposed methods are explained. The second chapter presents the included publications.