activity
20152019
most citedALFA: Agglomerative Late Fusion Algorithm for Object Detection

8 citations · 25 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV20198 cited

ALFA: Agglomerative Late Fusion Algorithm for Object Detection

Evgenii Razinkov, Iuliia Saveleva, Jiři Matas

We propose ALFA - a novel late fusion algorithm for object detection. ALFA is based on agglomerative clustering of object detector predictions taking into consideration both the bo…

cs.CV20192 cited

ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019

Nibal Nayef, Yash Patel, Michal Busta +8

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the go…

cs.CV2019

CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark

Alan Lukežič, Ugur Kart, Jani Käpylä +4

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…

cs.CV20194 cited

Performance Evaluation Methodology for Long-Term Visual Object Tracking

Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…

cs.CV20197 cited

Progressive NAPSAC: sampling from gradually growing neighborhoods

Daniel Barath, Maksym Ivashechkin, Jiri Matas

We propose Progressive NAPSAC, P-NAPSAC in short, which merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods. Exploiting the f…

cs.CV2019

Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm

Daniel Barath, Jiri Matas

The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting. The method interleaves sampling and consolidation of the current data interpretation vi…