NDLI: Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras

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Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras

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Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras

Content Provider	MDPI
Author	Jung, Jaehoon Yoon, Inhye Lee, Seungwon Paik, Joonki
Abstract	Since it is impossible for surveillance personnel to keep monitoring videos from a multiple camera-based surveillance system, an efficient technique is needed to help recognize important situations by retrieving the metadata of an object-of-interest. In a multiple camera-based surveillance system, an object detected in a camera has a different shape in another camera, which is a critical issue of wide-range, real-time surveillance systems. In order to address the problem, this paper presents an object retrieval method by extracting the normalized metadata of an object-of-interest from multiple, heterogeneous cameras. The proposed metadata generation algorithm consists of three steps: (i) generation of a three-dimensional (3D) human model; (ii) human object-based automatic scene calibration; and (iii) metadata generation. More specifically, an appropriately-generated 3D human model provides the foot-to-head direction information that is used as the input of the automatic calibration of each camera. The normalized object information is used to retrieve an object-of-interest in a wide-range, multiple-camera surveillance system in the form of metadata. Experimental results show that the 3D human model matches the ground truth, and automatic calibration-based normalization of metadata enables a successful retrieval and tracking of a human object in the multiple-camera video surveillance system.
File Size	10056704
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s16070963
Journal	Sensors
Issue Number	7
Volume Number	16
Language	English
Publisher Date	2016-06-24
Access Restriction	Open
Subject Keyword	video surveillance video retrieval automatic calibration metadata descriptor homology color clustering object tracking
Content Type	Text
Resource Type	Article

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