On the Statistical Determination of Optimal Camera Configurations in Large Scale Surveillance Networks

Junbin Liu¹, Clinton Fookes¹, Tim Wark², and Sridha Sridharan¹

¹Image & Video Research Laboratory, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia
junbin.liu@gmail.com
c.fookes@qut.edu.au
s.sridhara@qut.edu.au

²CSIRO ICT Centre, 1 Technology Court, Pullenvale, QLD 4069, Australia
tim.wark@csiro.au

Abstract. The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.

Keywords: Camera placement, optimization, resersible jump Markov chain Monte Carlo, simulated annealing

LNCS 7572, p. 44 ff.

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