A Unified Hierarchical Appearance Model for People Re-identification Using Multi-view Vision Sensors

Kao, Jau-Hong; Lin, Chih-Yang; Wang, Wen-How; Wu, You

doi:10.1007/978-3-540-89796-5_57

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Kao etc. [12] used color information to form a hierarchical color structures for each image. For different people, a set of images is used and makes each node in the hierarchical color structures a Gaussian mixture model.…”

Section: People Re-identification Methods Based On Human Appearancementioning

confidence: 99%

A framework for semantic people description in multi-camera surveillance systems

Zhou

Wang

Teoh

2016

Image and Vision Computing

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Section: People Re-identification Methods Based On Human Appearancementioning

confidence: 99%

A framework for semantic people description in multi-camera surveillance systems

Zhou

Wang

Teoh

2016

Image and Vision Computing

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“…The soft-biometrics are related to people's appearance, such as color, clothing, and height. The work proposed in [2], an appearance model represented by a hierarchical structure where each node maintains a color Gaussian mixture model (GMM), is proposed. The identification task is performed with a Bayesian decision approach.…”

Section: Overviewmentioning

confidence: 99%

“…Camera surveillance is more than a set of monitors connected to some cameras, in fact, it can actually be seen as a powerful technology for security control. One of the main problems in a multiple camera system is that people's appearance changes as observed from one camera to another [2]. Usually, a person's visual identification can be either based on biometric (face, iris, fingerprint) or soft-biometric features (appearance, height, clothes).…”

Section: Introductionmentioning

confidence: 99%

Soft-biometrics evaluation for people re-identification in uncontrolled multi-camera environments

Moctezuma¹,

Conde

Diego

et al. 2015

J Image Video Proc.

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A novel method for person identification based on soft-biometrics and oriented to work in real video surveillance environments is proposed in this paper. Thus, an evaluation of relevance's level of several appearance features is carried out with this purpose. First, a bag-of-soft-biometric features related to color, texture, local features, and geometry are extracted from individuals. The relevance of each feature has been deeply analyzed through different proposed methods. Features are ranked and weighted according to their relevance value. Later, each method is evaluated under two different scenarios: mono-camera and multi-camera surveillance images. In order to test the system in a realistic way, it has been evaluated over standard databases in the surveillance community: PETS 2006, PETS 2009, CAVIAR, SAIVT-SoftBio, and CAVIAR4REID. Moreover, a new database was acquired at Adolfo Suarez Madrid-Barajas international airport. This database was acquired under regular conditions and infrastructure of the Barajas airport, no additional camera or special settings were installed for this purpose. An analysis of relevance for each feature acquired in these two scenarios is presented. The results obtained demonstrate the promising potential of the soft-biometric approach. Finally, an optimal system configuration according to each scenario is obtained.

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“…Then Fisher Linear Discriminants was used to perform appearance-based on-line classification. Kao etc [63]. used color information to form a hierarchical color structures for each image.…”

mentioning

confidence: 99%

Robust multiple people tracking and re-identification in cluttered environment

Zhou¹

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A Unified Hierarchical Appearance Model for People Re-identification Using Multi-view Vision Sensors

Cited by 8 publications

References 9 publications

A framework for semantic people description in multi-camera surveillance systems

A framework for semantic people description in multi-camera surveillance systems

Soft-biometrics evaluation for people re-identification in uncontrolled multi-camera environments

Robust multiple people tracking and re-identification in cluttered environment

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