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Petr Sojka

Masaryk University, Faculty of Informatics

21 papers hereh-index 186.5k citations209 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4
  • last author16

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.CV5
  • cs.IR5
  • cs.DL2
affiliations
  • Masaryk University, Faculty of Informatics
Homepage
same name
  • Petr Sojka — 1 paper
  • Petr Sojka — 1 paper, h 2
  • Petr Sojka — 1 paper, h 2
  • Petr Sojka — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedGait Recognition from Motion Capture Data

43 citations · 141 across the 21 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 43 cited

Gait Recognition from Motion Capture Data

Michal Balazia, Petr Sojka

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art wi…

cs.IR2017

Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines

Jan Rygl, Jan Pomikálek, Radim Řehůřek +3

Vector representations and vector space modeling (VSM) play a central role in modern machine learning. We propose a novel approach to `vector similarity searching' over dense seman…

cs.CV2017★ 1 cited

You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data

Michal Balazia, Petr Sojka

This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video…

cs.CV2017★ 7 cited

An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

Michal Balazia, Petr Sojka

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition fro…

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