published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

A new background subtraction algorithm is proposed based on using a subspace model. The key components of the algorithm include a novel method for initializing the subspace and a robust update framework for continuously learning and improving the model. Unlike traditional subspace techniques the proposed approach does not require supervised or lengthy training data upfront, but instead is bootstrapped using a single background frame and exploiting spatial information in place of temporal data to generate pixel statistics for the model. The update framework allows for intelligently updating the model and re-initialization if required as determined by the algorithm. Experimental results indicate that the proposed subspace algorithm out performed traditional subspace approaches and was comparable to and sometimes better than leading standard pixel-based techniques on several standard background subtraction data sets. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-08-07 15:42:26.205

No description available.

Thesis (MSc)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

Automated visual recognition and detection processes are becoming increasingly prevalent in almost all scientific fields and being currently implemented in many fields of industry. In most cases, systems are painstakingly designed and developed in order to detect only a single and specific object or property of an object. The objective of this project was to create a framework of development in which any object distinguishable in a two-dimensional digital image could be analyzed and subsequently detected in other images. Furthermore, as new methods are developed, they could be easily incorporated into this framework to ultimately improve the performance of the system. / This thesis describes a highly adaptable, general-application visual detection system as well as several innovative methods for the description of objects without which such adaptivity would be impossible. Two-dimensional, still images are analyzed and objects of interest can be introduced to the system. Objects are then described by a variety of properties through derived attributes and stored in a database. Occurrences of these objects can then be detected in future images through comparisons to selected models. The system is fully expandable in that new properties and comparison techniques or criteria can be added as they are developed and as their need becomes apparent. The system is presented with a basic set of attribute representations and methods of comparison, and their development and origin are described in detail. The database structure is outlined and the process by which new properties and comparative methods can be added is described. Seventeen different images containing nearly two thousand separate objects were searched for various model objects and the average classification accuracy was 98.3%. In most images, more than 100 object classifications could be performed per second at an accuracy higher than 95% when no higher order analyses were required.

This thesis explores three object tracking algorithms for image sequences. These algorithms include the ensemble tracker, the EM-like mean-shift colour-histogram tracker, and the wandering-stable-lost scale-invariant feature transform (WSL-SIFT) tracker. The algorithms are radically different from one another. Despite their differences, they are evaluated on the same publicly available, moderately sized, research data sets which include 129 test cases in 13 different scenes. The results aid in fostering an understanding of their respective behaviours and in highlighting their flaws and failures. Lastly, an implementation setup is described that is suited to large-scale, grid computing, batch testing of these algorithms. Results clearly indicate that none of the evaluated trackers are suited to general purpose use. However, one may intelligently choose a tracker for a well-defined application by analysing the known scene characteristics.

Thesis (M. Phil.)--University of Hong Kong, 2008. / Also available in print.

Thesis (M.S.)--Case Western Reserve University, 2009 / Department of Computer Engineering Abstract Title from OhioLINK abstract screen (viewed on 13 April 2009) Available online via the OhioLINK ETD Center

Thesis (M.S.)--Ohio University, November, 1989. / Title from PDF t.p.