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Automatic inference of geometric camera parameters and intercamera topology in uncalibrated disjoint surveillance cameras

Author: Hollander, R.J.M. den · Bouma, H. · Baan, J. · Eendebak, P.T. · Rest, J.H.C. van
Publisher: SPIE
Place: Bellingham,WA
Source:Burgess, al., Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII, 21 September 2015, Toulouse France
Proceedings of SPIE
Identifier: 528829
doi: doi:10.1117/12.2194435
Article number: 96520D
Keywords: Image processing · Cameras · Surveillance · Video · Calibration · Analytics · Surveillance systems · Defence Research · Defence, Safety and Security · Observation, Weapon & Protection Systems Human and Operational Modelling · II - Intelligent Imaging NO - Networked Organisations · TS - Technical Sciences ; ELSS


Person tracking across non-overlapping cameras and other types of video analytics benefit from spatial calibration information that allows an estimation of the distance between cameras and a relation between pixel coordinates and world coordinates within a camera. In a large environment with many cameras, or for frequent ad-hoc deployments of cameras, the cost of this calibration is high. This creates a barrier for the use of video analytics. Automating the calibration allows for a short configuration time, and the use of video analytics in a wider range of scenarios, including ad-hoc crisis situations and large scale surveillance systems. We show an autocalibration method entirely based on pedestrian detections in surveillance video in multiple non-overlapping cameras. In this paper, we show the two main components of automatic calibration. The first shows the intra-camera geometry estimation that leads to an estimate of the tilt angle, focal length and camera height, which is important for the conversion from pixels to meters and vice versa. The second component shows the inter-camera topology inference that leads to an estimate of the distance between cameras, which is important for spatio-temporal analysis of multi-camera tracking. This paper describes each of these methods and provides results on realistic video data. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).