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Employing Shadows for Multi-Person Tracking Based on a Single RGB-D Camera.
Gai, Wei; Qi, Meng; Ma, Mingcong; Wang, Lu; Yang, Chenglei; Liu, Juan; Bian, Yulong; de Melo, Gerard; Liu, Shijun; Meng, Xiangxu.
Afiliação
  • Gai W; School of Software, Shandong University, Jinan 250101, China.
  • Qi M; School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
  • Ma M; School of Software, Shandong University, Jinan 250101, China.
  • Wang L; School of Software, Shandong University, Jinan 250101, China.
  • Yang C; School of Software, Shandong University, Jinan 250101, China.
  • Liu J; Engineering Research Center of Digital Media Technology, MOE, Jinan 250101, China.
  • Bian Y; School of Software, Shandong University, Jinan 250101, China.
  • de Melo G; School of Software, Shandong University, Jinan 250101, China.
  • Liu S; Department of Computer Science 110 Frelinghuysen Road Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8019, USA.
  • Meng X; School of Software, Shandong University, Jinan 250101, China.
Sensors (Basel) ; 20(4)2020 Feb 15.
Article em En | MEDLINE | ID: mdl-32075274
ABSTRACT
Although there are many algorithms to track people that are walking, existing methods mostly fail to cope with occluded bodies in the setting of multi-person tracking with one camera. In this paper, we propose a method to use people's shadows as a clue to track them instead of treating shadows as mere noise. We introduce a novel method to track multiple people by fusing shadow data from the RGB image with skeleton data, both of which are captured by a single RGB Depth (RGB-D) camera. Skeletal tracking provides the positions of people that can be captured directly, while their shadows are used to track them when they are no longer visible. Our experiments confirm that this method can efficiently handle full occlusions. It thus has substantial value in resolving the occlusion problem in multi-person tracking, even with other kinds of cameras.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reconhecimento Automatizado de Padrão / Fotografação Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Reconhecimento Automatizado de Padrão / Fotografação Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China