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.