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Abstract
Multiple camera systems may be divided into multiple overlapping and non-overlapping camera systems. Both are widely used for image and video analysis tasks, object analysis, computer graphics and surveillance. Visual matching and tracking of humans in multiple camera systems is an important component of many such systems. For both overlapping and non-overlapping camera systems, recognising the same person in different cameras is the main challenge. Different methods have been developed to match and track humans in video.
This thesis proposes novel methods for human matching and tracking in multiple camera systems, both overlapping and non-overlapping. It presents a frame-based human matching method for multiple cameras that combines feature point descriptions and geometric constraints between feature points to achieve better human matching results. It also presents a sequence-based human appearance matching method, based on model-adaptive Mean-Shift tracking and key patch selection to improve the human matching accuracy. Through key patch re-initialization, humans can also be tracked in multiple camera systems.
The proposed methods have been tested on videos obtained from the Smart Room in School of Computer Science and Engineering, University of New South Wales. Experimental analysis presented in this thesis shows that the proposed methods can match and track humans stably.