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.
Highlights
Multiple camera-based video surveillance systems are producing a huge amount of data every day
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
In order to solve the common problems of existing video retrieval methods, this paper presents a normalized metadata generation method from a very wide-range surveillance system to retrieve an object-of-interest
Summary
Multiple camera-based video surveillance systems are producing a huge amount of data every day. In order to retrieve meaningful information from the large data set, normalized metadata should be extracted to identify and track an object-of-interest acquired by multiple, heterogeneous cameras. Ge et al. Sensors 2016, 16, 963 detected and tracked multiple pedestrians using sociological models to generate the trajectory data for video feature indexing [8]. The common challenge of existing video indexing and retrieval methods is to summarize infrequent events from a large dataset generated using multiple, heterogeneous cameras. In order to solve the common problems of existing video retrieval methods, this paper presents a normalized metadata generation method from a very wide-range surveillance system to retrieve an object-of-interest. A human model-based automatic calibration algorithm and the corresponding metadata retrieval method are respectively presented in Sections 3 and 4.
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