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Stephen J. Maybank
Researcher at Birkbeck, University of London
Publications - 172
Citations - 17723
Stephen J. Maybank is an academic researcher from Birkbeck, University of London. The author has contributed to research in topics: Video tracking & Motion estimation. The author has an hindex of 50, co-authored 166 publications receiving 15225 citations. Previous affiliations of Stephen J. Maybank include University of Oxford & University of Reading.
Papers
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Proceedings ArticleDOI
On plane-based camera calibration: A general algorithm, singularities, applications
Peter Sturm,Stephen J. Maybank +1 more
TL;DR: A general algorithm for plane-based calibration that can deal with arbitrary numbers of views and calibration planes and it is easy to incorporate known values of intrinsic parameters is presented.
Journal ArticleDOI
A Survey on Visual Content-Based Video Indexing and Retrieval
TL;DR: Methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, and video retrieval including query interfaces are analyzed.
Journal ArticleDOI
Geometric Mean for Subspace Selection
TL;DR: Preliminary experimental results show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
Journal ArticleDOI
A system for learning statistical motion patterns
TL;DR: Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.
Proceedings ArticleDOI
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
TL;DR: A Residual Attentional Siamese Network (RASNet) for high performance object tracking that mitigates the over-fitting problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation of representation learning and discriminator learning.