Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras
AbstractSince 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
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Jung, J.; Yoon, I.; Lee, S.; Paik, J. Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras. Sensors 2016, 16, 963.
Jung J, Yoon I, Lee S, Paik J. Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras. Sensors. 2016; 16(7):963.Chicago/Turabian Style
Jung, Jaehoon; Yoon, Inhye; Lee, Seungwon; Paik, Joonki. 2016. "Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras." Sensors 16, no. 7: 963.
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