Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/73108
Title: Gotcha-I: a multiview human videos dataset
Authors: Barra, Paola
Bisogni, Carmen
Nappi, Michele
Freire Obregón, David Sebastián 
Castrillón Santana, Modesto Fernando 
UNESCO Clasification: 2405 Biometría
120317 Informática
120304 Inteligencia artificial
Keywords: Biometric
Body worn cameras
Dataset
Face
Gait, et al
Issue Date: 2020
Journal: Communications in Computer and Information Science 
Conference: 7th International Symposium on Security in Computing and Communications, SSCC 2019 
Abstract: The growing need of security in large open spaces led to the need to use video capture of people in different context and illumination and with multiple biometric traits as head pose, body gait, eyes, nose, mouth, and further more. All these traits are useful for a multibiometric identification or a person re-identification in a video surveillance context. Body Worn Cameras (BWCs) are used by the police of different countries all around the word and their use is growing significantly. This raises the need to develop new recognition methods that consider multibiometric traits on person re-identification. The purpose of this work is to present a new video dataset called Gotcha-I. This dataset has been obtained using more mobile cameras to adhere to the data of BWCs. The dataset includes videos from 62 subjects in indoor and outdoor environments to address both security and surveillance problem. During these videos, subjects may have a different behavior in videos such as freely, path, upstairs, avoid the camera. The dataset is composed by 493 videos including a set of 180° videos for each face of the subjects in the dataset. Furthermore, there are already processed data, such as: the 3D model of the face of each subject with all the poses of the head in pitch, yaw and roll; and the body keypoint coordinates of the gait for each video frame. It’s also shown an application of gender recognition performed on Gotcha-I, confirming the usefulness and innovativeness of the proposed dataset.
URI: http://hdl.handle.net/10553/73108
ISBN: 978-981-15-4824-6
ISSN: 1865-0929
DOI: 10.1007/978-981-15-4825-3_17
Source: Communications in Computer and Information Science [ISSN 1865-0929], v. 1208 CCIS, p. 213-224, (Enero 2020)
Appears in Collections:Actas de congresos
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