Title
Automatically assessing properties of dynamic cameras for camera selection and rapid deployment of video-content-analysis tasks in large-scale ad-hoc networks
Author
den Hollander, R.J.M.
Bouma, H.
van Rest, J.H.C.
ten Hove, J.M.
ter Haar, F.B.
Burghouts, G.J.
Publication year
2017
Abstract
Video analytics is essential for managing large quantities of raw data that are produced by video surveillance systems (VSS) for the prevention, repression and investigation of crime and terrorism. Analytics is highly sensitive to changes in the scene, and for changes in the optical chain so a VSS with analytics needs careful configuration and prompt maintenance to avoid false alarms. However, there is a trend from static VSS consisting of fixed CCTV cameras towards more dynamic VSS deployments over public/private multi-organization networks, consisting of a wider variety of visual sensors, including pan-tilt-zoom (PTZ) cameras, body-worn cameras and cameras on moving platforms. This trend will lead to more dynamic scenes and more frequent changes in the optical chain, creating structural problems for analytics. If these problems are not adequately addressed, analytics will not be able to continue to meet end users’ developing needs. In this paper, we present a three-part solution for managing the performance of complex analytics deployments. The first part is a register containing meta data describing relevant properties of the optical chain, such as intrinsic and extrinsic calibration, and parameters of the scene such as lighting conditions or measures for scene complexity (e.g. number of people). A second part frequently assesses these parameters in the deployed VSS, stores changes in the register, and signals relevant changes in the setup to the VSS administrator. A third part uses the information in the register to dynamically configure analytics tasks based on VSS operator input. In order to support the feasibility of this solution, we give an overview of related state-of-the-art technologies for autocalibration (self-calibration), scene recognition and lighting estimation in relation to person detection. The presented solution allows for rapid and robust deployment of Video Content Analysis (VCA) tasks in large scale ad-hoc networks.
Subject
2015 Observation, Weapon & Protection Systems 2015 Human & Operational Modelling
II - Intelligent Imaging NO - Networked Organisations
ELSS - Earth, Life and Social Sciences TS - Technical Sciences
To reference this document use:
http://resolver.tudelft.nl/uuid:2e20a374-a7d4-40ae-8513-f6177a41bb3e
TNO identifier
781165
Source
Warsaw, SPIE, Vol. 10441, (2017).
Document type
conference paper