VOE: A new sparsity-based camera network placement framework

Abstract In this paper, we propose a stepwise sparsity-based framework for camera network placement. Unlike most previous methods which are developed for specific tasks, our approach is universal and can generalize well for different application scenarios. There are three steps in our approach: visibility analysis, optimization and evaluation (VOE), which are employed sequentially and iteratively. First, we use a cascaded visibility filter model to construct a visibility matrix, where each column describes the appearance representation of the surveillance area. Then, we formulate camera network layout as a sparse representation problem, and employ an l 1 -optimization algorithm to obtain a feasible solution. Our framework is general enough and applicable to various objectives in practical applications. Experiment results are presented to show the effectiveness and efficiency of the proposed framework.

[1]  Stan Sclaroff,et al.  Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements , 2006, Comput. Vis. Image Underst..

[2]  Naoki Wakamiya,et al.  Challenging issues in visual sensor networks , 2009, IEEE Wireless Communications.

[3]  T. A. Van der Laan,et al.  Intelligent Multi-camera Video Surveillance , 2012 .

[4]  Xiaogang Wang,et al.  Intelligent multi-camera video surveillance: A review , 2013, Pattern Recognit. Lett..

[5]  Sridha Sridharan,et al.  Optimal Camera Planning Under Versatile User Constraints in Multi-Camera Image Processing Systems , 2014, IEEE Transactions on Image Processing.

[6]  Nael B. Abu-Ghazaleh,et al.  Coverage algorithms for visual sensor networks , 2013, TOSN.

[7]  Lei Deng,et al.  Surveillance of a 2D Plane Area with 3D Deployed Cameras , 2014, Sensors.

[8]  Kemal Akkaya,et al.  Optimal Camera Placement for Providing Angular Coverage in Wireless Video Sensor Networks , 2014, IEEE Transactions on Computers.

[9]  Mongi A. Abidi,et al.  Can You See Me Now? Sensor Positioning for Automated and Persistent Surveillance , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Bang Wang,et al.  Coverage problems in sensor networks: A survey , 2011, CSUR.

[11]  Nicola Conci,et al.  Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization , 2013, 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC).

[12]  Nicola Conci,et al.  Camera positioning for global and local coverage optimization , 2012, 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC).

[13]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[14]  Y. Morsly,et al.  Particle Swarm Optimization Inspired Probability Algorithm for Optimal Camera Network Placement , 2012, IEEE Sensors Journal.

[15]  D. Puccinelli,et al.  Wireless sensor networks: applications and challenges of ubiquitous sensing , 2005, IEEE Circuits and Systems Magazine.

[16]  Kemal Akkaya,et al.  Providing multi-perspective event coverage in wireless multimedia sensor networks , 2010, IEEE Local Computer Network Conference.

[17]  Yu-Chee Tseng,et al.  $k$-Angle Object Coverage Problem in a Wireless Sensor Network , 2012, IEEE Sensors Journal.

[18]  Ju-Jang Lee,et al.  Multiobjective Optimization Approach for Sensor Arrangement in A Complex Indoor Environment , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  Jian Zhao,et al.  Approximate Techniques in Solving Optimal Camera Placement Problems , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[20]  Bang Jun Lei,et al.  Constrained particle swarm algorithms for optimizing coverage of large-scale camera networks with mobile nodes , 2013, Soft Comput..

[21]  Mihaela Cardei,et al.  Coverage Problems in Sensor Networks , 2013 .

[22]  Simone Gasparini,et al.  Camera Models and Fundamental Concepts Used in Geometric Computer Vision , 2011, Found. Trends Comput. Graph. Vis..

[23]  Richard Cole,et al.  Visibility Problems for Polyhedral Terrains , 2018, J. Symb. Comput..

[24]  A. Gasteratos,et al.  Optimum multi-camera arrangement using a bee colony algorithm , 2012, 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings.

[25]  Stan Sclaroff,et al.  Optimal Placement of Cameras in Floorplans to Satisfy Task Requirements and Cost Constraints , 2004 .

[26]  Zhaolin Cheng,et al.  Calibrating Distributed Camera Networks , 2008, Proceedings of the IEEE.

[27]  Ian F. Akyildiz,et al.  Wireless sensor networks , 2007 .

[28]  Xiang Chen,et al.  Modeling Coverage in Camera Networks: A Survey , 2012, International Journal of Computer Vision.