Deep Neural Networks for Visual Object Tracking: An Investigation of Performance Optimization
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Li, PeixiaAbstract
This thesis advances visual object tracking by introducing four key enhancements that address current limitations in training data, network architecture, and tracking methodologies. It proposes a refined sampling strategy for Siamese Networks to enrich training data and develops a ...
See moreThis thesis advances visual object tracking by introducing four key enhancements that address current limitations in training data, network architecture, and tracking methodologies. It proposes a refined sampling strategy for Siamese Networks to enrich training data and develops a more efficient Partially Siamese Network through neural architecture search, achieving superior performance on benchmarks. The work further streamlines tracking with a new transformer-based pipeline and breaks ground with a speech-guided tracking framework, improving human-machine collaboration. These advancements are thoroughly validated, marking significant progress in the visual object tracking domain.
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See moreThis thesis advances visual object tracking by introducing four key enhancements that address current limitations in training data, network architecture, and tracking methodologies. It proposes a refined sampling strategy for Siamese Networks to enrich training data and develops a more efficient Partially Siamese Network through neural architecture search, achieving superior performance on benchmarks. The work further streamlines tracking with a new transformer-based pipeline and breaks ground with a speech-guided tracking framework, improving human-machine collaboration. These advancements are thoroughly validated, marking significant progress in the visual object tracking domain.
See less
Date
2023Licence
Copyright All Rights ReservedRights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Electrical and Information EngineeringAwarding institution
The University of SydneyShare