Real-time visual tracking using image processing and filtering methods

Visual tracking;Image processing;Nonlinear filtering;Statistical estimation;Shape modeling.

Ha, Jin-cheol ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Visual tracking;    Image processing;    Nonlinear filtering;    Statistical estimation;    Shape modeling.;   

Others  :  https://smartech.gatech.edu/bitstream/1853/28177/1/Ha_Jincheol_200805_phd_multmedia2.avi

美国|英语

来源: SMARTech Repository

【 摘 要 】

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 majorsubjects addressed by this work are: 1. The development of fast and robust imagesegmentation 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 onboardcomputer 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 specificcategories, 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 theoutlines 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 dimensionalfunction 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 asfast 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 fromthe 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 theextraction 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 occurwhen 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 thesisconcentrates 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 thetarget. 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 andthe 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 theappearing/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.