Normalized Metadata Generation for Human Retrieval Using Multiple Video Surveillance Cameras.

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

SUBMITTER: Jung J 

PROVIDER: S-EPMC4969833 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature