A novel approach to represent the profile of objects using Gaussian models is presented. The profile is a representation of the object and its surrounding regions. The object profile can be viewed as a comprehensive feature of that object and its surrounding regions. Different algorithms to estimate the profile are described. Geometric descriptors of the model are also proposed. The profile model is empirically shown to be effective and easily applicable to certain object recognition and segmentation tasks. Application experiments include modeling thin and thick objects as straight-lines, curves, and blobs using different primitives such as gray-level intensities, RGB, and HSV color. The datasets used for empirical validation are quite challenging. Datasets include images and videos corresponding to outdoor video, most of them with moving cameras. Some of the typical problems faced with the used datasets are: digital scaling, compression artifacts, camera jitter, weather effects, and cluttered backgrounds. We demonstrate the potential of leveraging the context of objects of interest as a part of an online detection process. Sample applications including detection of wires, sea horizon, street, and vehicles in outdoor videos are considered.

This work presents a new technique for automatic detection of marine vehicles in images and video of open sea. Users of such system include border guards, military, port safety, flow management, and sanctuary protection personnel. The source of images and video is a digital camera or a camcorder which is placed on a buoy or stationary mounted in a harbor facility. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them for the presence of marine vehicles. The goal of the system is to detect an approximate window around the ship. The proposed computer vision-based algorithm combines a horizon detection method with edge detection and postprocessing. Several datasets of still images are used to evaluate the performance of the proposed technique. For video sequences the original algorithm is further enhanced with a tracking algorithm that uses Kalman filter. A separate dataset of 30 video sequences 10 seconds each is used to test its performance. Promising results of the detection of ships are discussed and necessary improvements for achieving better performance are suggested.

Visual surveillance in the maritime domain has been explored for more than a decade. Although it has produced a number of working systems and resulted in a mature technology, surveillance has been restricted to the port facilities or areas close to the coastline assuming a fixed-camera scenario. This dissertation presents several contributions in the domain of maritime surveillance. First, a novel algorithm for open-sea visual maritime surveillance is introduced. We explore a challenging situation with a camera mounted on a buoy or other floating platform. The developed algorithm detects, localizes, and tracks ships in the field of view of the camera. Specifically, our method is uniquely designed to handle a rapidly moving camera. Its performance is robust in the presence of a random relatively-large camera motion. In the context of ship detection, a new horizon detection scheme for a complex maritime domain is also developed. Second, the performance of the ship detection algorithm is evaluated on a dataset of 55,000 images. Accuracy of detection of up to 88% of ships is achieved. Lastly, we consider the topic of detection of the vanishing line of the ocean surface plane as a way to estimate the horizon in difficult situations. This allows extension of the ship-detection algorithm to beyond open-sea scenarios.

This master's thesis deals with development of mobile app for measuring the range from the preceding vehicle in traffic using visual-based methods. This paper describes implementation of computer vision algorithms of detection and tracing objects, detection of horizon on desktop and mobile devices. Also deals with visual-based range measuring without any other mechanisms. The output of the work is implemented detectors of vihicles and horizon using OpenCV library on the Windows platfom and draft of user inerface of a mobile phone aplication on the Android platform.