MuPeG—The Multiple Person Gait Framework

Autor: Rubén Delgado-Escaño, Francisco M. Castro, Nicolás Guil, Manuel J. Marín-Jiménez, Julián Ramos Cózar
Jazyk: angličtina
Rok vydání: 2020
Předmět:
gait framework
Computer science
0211 other engineering and technologies
Extrapolation
02 engineering and technology
lcsh:Chemical technology
Machine learning
computer.software_genre
Biochemistry
Article
Analytical Chemistry
Task (project management)
gait recognition
Gait (human)
0202 electrical engineering
electronic engineering
information engineering
Humans
lcsh:TP1-1185
Computer Simulation
Electrical and Electronic Engineering
Instrumentation
Gait
021110 strategic
defence & security studies
business.industry
gait dataset
augmented dataset
Atomic and Molecular Physics
and Optics
gait recognition
gait framework
gait dataset
multiple subjects
augmented dataset
Pattern Recognition
Physiological
multiple subjects
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Algorithms
Zdroj: Sensors
Volume 20
Issue 5
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 5, p 1358 (2020)
ISSN: 1424-8220
DOI: 10.3390/s20051358
Popis: Gait recognition is being employed as an effective approach to identify people without requiring subject collaboration. Nowadays, developed techniques for this task are obtaining high performance on current datasets (usually more than 90 % of accuracy). However, those datasets are simple as they only contain one subject in the scene at the same time. This fact limits the extrapolation of the results to real world conditions where, usually, multiple subjects are simultaneously present at the scene, generating different types of occlusions and requiring better tracking methods and models trained to deal with those situations. Thus, with the aim of evaluating more realistic and challenging situations appearing in scenarios with multiple subjects, we release a new framework (MuPeG) that generates augmented datasets with multiple subjects using existing datasets as input. By this way, it is not necessary to record and label new videos, since it is automatically done by our framework. In addition, based on the use of datasets generated by our framework, we propose an experimental methodology that describes how to use datasets with multiple subjects and the recommended experiments that are necessary to perform. Moreover, we release the first experimental results using datasets with multiple subjects. In our case, we use an augmented version of TUM-GAID and CASIA-B datasets obtained with our framework. In these augmented datasets the obtained accuracies are 54.8 % and 42.3 % whereas in the original datasets (single subject), the same model achieved 99.7 % and 98.0 % for TUM-GAID and CASIA-B, respectively. The performance drop shows clearly that the difficulty of datasets with multiple subjects in the scene is much higher than the ones reported in the literature for a single subject. Thus, our proposed framework is able to generate useful datasets with multiple subjects which are more similar to real life situations.
Databáze: OpenAIRE
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