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Sensors 2016, 16(7), 963; doi:10.3390/s16070963

Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras

1
Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
2
ADAS Camera Team, LG Electronics, 322 Gyeongmyeong-daero, Seo-gu, Incheon 22744, Korea
3
Software Development Team, Convergence R&D Center, LG Innotek, Gyeonggi-do 15588, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Xue-Bo Jin
Received: 20 March 2016 / Revised: 16 June 2016 / Accepted: 21 June 2016 / Published: 24 June 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)

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. View Full-Text
Keywords: video surveillance; video retrieval; automatic calibration; metadata descriptor; homology; color clustering; object tracking video surveillance; video retrieval; automatic calibration; metadata descriptor; homology; color clustering; object tracking
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jung, J.; Yoon, I.; Lee, S.; Paik, J. Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras. Sensors 2016, 16, 963.

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