Motion representation using composite energy features

This work tackles the segmentation of apparent-motion from a bottom-up perspective. When no information is available to build prior high-level models, the only alternative are bottom-up techniques. Hence, the whole segmentation process relies on the suitability of the low-level features selected to describe motion. A wide variety of low-level spatio-temporal features have been proposed so far. However, all of them suffer from diverse drawbacks. Here, we propose the use of composite energy features in bottom-up motion segmentation to solve several of these problems. Composite energy features are clusters of energy filters—pairs of band-pass filters in quadrature—each one sensitive to a different set of scale, orientation, direction of motion and speed. They are grouped in order to reconstruct independent motion patterns in a video sequence. A composite energy feature, this is, the response of one of these clusters of filters, can be built as a combination of the responses of the individual filters. Therefore, it inherits the desirable properties of energy filters but providing a more complete representation of motion patterns. In this paper, we will present our approach for integration of composite features based on the concept of Phase Congruence. We will show some results that illustrate the capabilities of this low-level motion representation and its usefulness in bottom-up motion segmentation and tracking.This work has been financially supported by the Ministry of Education and Science of the Spanish Government, through the Research Project TIN2006-08447.S

Multispectral persistent surveillance

The goal of a successful surveillance system to achieve persistence is to track everything that moves, all of the time, over the entire area of interest. The thrust of this thesis is to identify and improve upon the motion detection and object association aspect of this challenge by adding spectral information to the equation. Traditional motion detection and tracking systems rely primarily on single-band grayscale video, while more current research has focused on sensor fusion, specifically combining visible and IR data sources. A further challenge in covering an entire area of responsibility (AOR) is a limited sensor field of view, which can be overcome by either adding more sensors or multi-tasking a single sensor over multiple areas at a reduced frame rate. As an essential tool for sensor design and mission development, a trade study was conducted to measure the potential advantages of adding spectral bands of information in a single sensor with the intention of reducing sensor frame rates. Thus, traditional motion detection and object association algorithms were modified to evaluate system performance using five spectral bands (visible through thermal IR), while adjusting frame rate as a second variable. The goal of this research was to produce an evaluation of system performance as a function of the number of bands and frame rate. As such, performance surfaces were generated to assess relative performance as a function of the number of bands and frame rate