P
Pedro Martins
Researcher at University of Coimbra
Publications - 32
Citations - 7624
Pedro Martins is an academic researcher from University of Coimbra. The author has contributed to research in topics: Active appearance model & Point distribution model. The author has an hindex of 14, co-authored 32 publications receiving 6159 citations.
Papers
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Journal ArticleDOI
High-Speed Tracking with Kernelized Correlation Filters
TL;DR: A new kernelized correlation filter is derived, that unlike other kernel algorithms has the exact same complexity as its linear counterpart, which is called dual correlation filter (DCF), which outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite being implemented in a few lines of code.
Book ChapterDOI
Exploiting the circulant structure of tracking-by-detection with kernels
TL;DR: Using the well-established theory of Circulant matrices, this work provides a link to Fourier analysis that opens up the possibility of extremely fast learning and detection with the Fast Fourier Transform, which can be done in the dual space of kernel machines as fast as with linear classifiers.
Proceedings ArticleDOI
Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow
TL;DR: A novel concept is proposed that allows to explicitly integrate multi-source domains while the previous one uses the mean of all sources, which enables to model better the domain shift and take fully advantage of the training datasets.
Proceedings ArticleDOI
A nonparametric Riemannian framework on tensor field with application to foreground segmentation
TL;DR: This work presents a mathematically-sound framework for nonparametric modeling on tensor field to foreground segmentation and endow the tensor manifold with two well-founded Riemannian metrics, i.e. Affine-Invariant and Log-Euclidean.
Proceedings ArticleDOI
Accurate single view model-based head pose estimation
Pedro Martins,Jorge Batista +1 more
TL;DR: The overall performance of the proposed solution was evaluated comparing the results with a ground-truth data obtained by a pose planar approach, showing that orientations and head location were, on average, found within 2deg or 1 cm error standard deviations respectively.