scispace - formally typeset
J

Jiri Matas

Researcher at Czech Technical University in Prague

Publications -  359
Citations -  50878

Jiri Matas is an academic researcher from Czech Technical University in Prague. The author has contributed to research in topics: RANSAC & Video tracking. The author has an hindex of 78, co-authored 345 publications receiving 44739 citations. Previous affiliations of Jiri Matas include University of Surrey & IEEE Computer Society.

Papers
More filters
Proceedings ArticleDOI

The Seventh Visual Object Tracking VOT2019 Challenge Results

Matej Kristan, +179 more
TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Book ChapterDOI

The Visual Object Tracking VOT2014 challenge results

TL;DR: The evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset are presented, offering a more systematic comparison of the trackers.
Journal ArticleDOI

Rotation-Invariant Image and Video Description With Local Binary Pattern Features

TL;DR: The proposed video features can effectively deal with rotation variations of dynamic textures (DTs) and are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs.
Posted Content

COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

TL;DR: The COCO-Text dataset is described, which contains over 173k text annotations in over 63k images and presents an analysis of three leading state-of-the-art photo Optical Character Recognition (OCR) approaches on the dataset.
Proceedings ArticleDOI

Total recall II: Query expansion revisited

TL;DR: Three extensions to automatic query expansion are introduced: a method capable of preventing tf-idf failure caused by the presence of sets of correlated features, an improved spatial verification and re-ranking step that incrementally builds a statistical model of the query object and a learn relevant spatial context to boost retrieval performance.