TY - GEN
T1 - On the statistical determination of optimal camera configurations in large scale surveillance networks
AU - Liu, Junbin
AU - Fookes, Clinton
AU - Wark, Tim
AU - Sridharan, Sridha
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Camera placement
KW - optimization
KW - resersible jump Markov chain Monte Carlo
KW - simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=84867887674&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33718-5_4
DO - 10.1007/978-3-642-33718-5_4
M3 - Conference contribution
SN - 9783642337178
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 57
BT - Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
T2 - 12th European Conference on Computer Vision, ECCV 2012
Y2 - 7 October 2012 through 13 October 2012
ER -