Large-Scale Camera Network Topology Estimation by Lighting Variation

This paper proposes a scalable and robust algorithm to find connections between cameras in a large surveillance network, based solely on lighting variation. We show how to detect regions that are affected by lighting changes within each camera view, with limited data. Then, we establish the light-overlap connections and show that our algorithm can scale to hundreds of camera while maintaining high accuracy. We demonstrate our method on a campus network of 100 real cameras and 500 simulated cameras, and evaluate its accuracy and scalability.

[1]  Vijanth S. Asirvadam,et al.  Camera coverage prioritization to support security monitoring , 2016, 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS).

[2]  Anton van den Hengel,et al.  Finding Camera Overlap in Large Surveillance Networks , 2007, ACCV.

[3]  Mubarak Shah,et al.  Tracking across multiple cameras with disjoint views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Rhys Hill,et al.  Tracking hand-off in large surveillance networks , 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

[5]  Daniel Keren,et al.  Multi-Camera Topology Recovery from Coherent Motion , 2007, 2007 First ACM/IEEE International Conference on Distributed Smart Cameras.

[6]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[7]  Ivan W. Selesnick,et al.  Total variation denoising (an MM algorithm) , 2012 .

[8]  Anton van den Hengel,et al.  Large-Scale Camera Topology Mapping: Application to Re-identification , 2014, Person Re-Identification.

[9]  Anton van den Hengel,et al.  Camera Network Topology Estimation by Lighting Variation , 2015, 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[10]  S. Shankar Sastry,et al.  A Distributed Topological Camera Network Representation for Tracking Applications , 2010, IEEE Transactions on Image Processing.

[11]  Andrea Cavallaro,et al.  Automated Localization of a Camera Network , 2012, IEEE Intelligent Systems.

[12]  Nicola Conci,et al.  Global Coverage Maximization in PTZ-Camera Networks Based on Visual Quality Assessment , 2016, IEEE Sensors Journal.

[13]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..