GRIDDS - A Gait Recognition Image and Depth Dataset

• Type: Chapter or Part of a Book
• Date: 2019-10

Authors

João Ferreira Nunes (Author) Other; Pedro Miguel Moreira (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Book

Title: VipIMAGE 2019 - Proceedings of the VII ECCOMAS Thematic = Lecture Notes in Computational Vision and Biomechanics

Pages: 343-352

Publisher: Springer

Indexing

ISI Proceedings

Scopus - 1 Citation

Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00R-5AE

DOI: 10.1007/978-3-030-32040-9_36

Abstract (EN): Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10