Real-time visual tracking using image processing and filtering methods

Ha, Jin-cheol

Title:

Real-time visual tracking using image processing and filtering methods

dc.contributor.advisor	Johnson, Eric N.
dc.contributor.advisor	Tannenbaum, Allen R.
dc.contributor.author	Ha, Jin-cheol	en_US
dc.contributor.committeeMember	Calise, Anthony J.
dc.contributor.committeeMember	Feron, Eric
dc.contributor.committeeMember	Vela, Patricio A.
dc.contributor.department	Aerospace Engineering	en_US
dc.date.accessioned	2009-06-08T19:18:16Z
dc.date.available	2009-06-08T19:18:16Z
dc.date.issued	2008-04-01	en_US
dc.description.abstract	The main goal of this thesis is to develop real-time computer vision algorithms in order to detect and to track targets in uncertain complex environments purely based on a visual sensor. Two major subjects addressed by this work are: 1. The development of fast and robust image segmentation algorithms that are able to search and automatically detect targets in a given image. 2. The development of sound filtering algorithms to reduce the effects of noise in signals from the image processing. The main constraint of this research is that the algorithms should work in real-time with limited computing power on an onboard computer in an aircraft. In particular, we focus on contour tracking which tracks the outline of the target represented by contours in the image plane. This thesis is concerned with three specific categories, namely image segmentation, shape modeling, and signal filtering. We have designed image segmentation algorithms based on geometric active contours implemented via level set methods. Geometric active contours are deformable contours that automatically track the outlines of objects in images. In this approach, the contour in the image plane is represented as the zero-level set of a higher dimensional function. (One example of the higher dimensional function is a three-dimensional surface for a two-dimensional contour.) This approach handles the topological changes (e.g., merging, splitting) of the contour naturally. Although geometric active contours prevail in many fields of computer vision, they suffer from the high computational costs associated with level set methods. Therefore, simplified versions of level set methods such as fast marching methods are often used in problems of real-time visual tracking. This thesis presents the development of a fast and robust segmentation algorithm based on up-to-date extensions of level set methods and geometric active contours, namely a fast implementation of Chan-Vese's (active contour) model (FICVM). The shape prior is a useful cue in the recognition of the true target. For the contour tracker, the outline of the target can be easily disrupted by noise. In geometric active contours, to cope with deviations from the true outline of the target, a higher dimensional function is constructed based on the shape prior, and the contour tracks the outline of an object by considering the difference between the higher dimensional functions obtained from the shape prior and from a measurement in a given image. The higher dimensional function is often a distance map which requires high computational costs for construction. This thesis focuses on the extraction of shape information from only the zero-level set of the higher dimensional function. This strategy compensates for inaccuracies in the calculation of the shape difference that occur when a simplified higher dimensional function is used. This is named as contour-based shape modeling. Filtering is an essential element in tracking problems because of the presence of noise in system models and measurements. The well-known Kalman filter provides an exact solution only for problems which have linear models and Gaussian distributions (linear/Gaussian problems). For nonlinear/non-Gaussian problems, particle filters have received much attention in recent years. Particle filtering is useful in the approximation of complicated posterior probability distribution functions. However, the computational burden of particle filtering prevents it from performing at full capacity in real-time applications. This thesis concentrates on improving the processing time of particle filtering for real-time applications. In principle, we follow the particle filter in the geometric active contour framework. This thesis proposes an advanced blob tracking scheme in which a blob contains shape prior information of the target. This scheme simplifies the sampling process and quickly suggests the samples which have a high probability of being the target. Only for these samples is the contour tracking algorithm applied to obtain a more detailed state estimate. Curve evolution in the contour tracking is realized by the FICVM. The dissimilarity measure is calculated by the contour based shape modeling method and the shape prior is updated when it satisfies certain conditions. The new particle filter is applied to the problems of low contrast and severe daylight conditions, to cluttered environments, and to the appearing/disappearing target tracking. We have also demonstrated the utility of the filtering algorithm for multiple target tracking in the presence of occlusions. This thesis presents several test results from simulations and flight tests. In these tests, the proposed algorithms demonstrated promising results in varied situations of tracking.	en_US
dc.description.degree	Ph.D.	en_US
dc.identifier.uri	http://hdl.handle.net/1853/28177
dc.publisher	Georgia Institute of Technology	en_US
dc.subject	Visual tracking	en_US
dc.subject	Image processing	en_US
dc.subject	Nonlinear filtering	en_US
dc.subject	Statistical estimation	en_US
dc.subject	Shape modeling.	en_US
dc.subject.lcsh	Computer vision
dc.subject.lcsh	Image processing Digital techniques
dc.subject.lcsh	Real-time data processing
dc.subject.lcsh	Algorithms
dc.title	Real-time visual tracking using image processing and filtering methods	en_US
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Johnson, Eric N.
local.contributor.corporatename	College of Engineering
local.contributor.corporatename	Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries	Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication	175a1f2b-c14e-4c43-a9e5-136fb7f8e5d0
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication	a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication	f6a932db-1cde-43b5-bcab-bf573da55ed6