Object tracking by a generalized Hough transform

Jon Natanael Muhovič (2014) Object tracking by a generalized Hough transform. EngD thesis.

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Abstract

Visual object tracking is a very diverse and useful area of computer vision. There are many different approaches to solving this problem and the goal of the thesis is to first present some of the methods that are used for implementation of state-of-the-art tracking algorithms. Secondly, the analysis of a concrete algorithm that tracks the object by using generalized Hough transform and lastly to design and implement some enhancements that boost the algorithm's robustness and accuracy. The proposed enhancements are based on Harris corner detection, Kalman filter and a segmentation algorithm that uses Markov random fields. The result of the thesis is thus an improved algorithm, implemented in C++ with added methods that improve its performance. Practical experiments were carried out in a framework designed for testing tracking algorithms by using diverse and difficult video sequences. Experiment results clearly show the improvements caused by the proposed methods.

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