On-line configuration of large scale surveillance networks using mobile smart camera

Distributed camera networks represent an emerging trend for the incorporation of mobile smart cameras in next generation large-scale surveillance systems. To achieve the great benefits of mobile smart camera, two challenging issues, the limited computational capabilities of a smart camera and the energy constraints of its battery-powered mobile platform, should be faced. This paper investigates the computation-efficient algorithm for the on-line network configuration, and the energy-saving method to implement this configuration.We design DMSA, a distributed mean-shift based heuristic algorithm for optimal coverage in dynamic surveillance environment. DMSA presents a distributed camera configuration for adapting the varying surveillance requirement, which achieves computation efficiency as well as coverage performance by exploiting on the tracking-inspired local search and cluster-based cooperating coverage. Furthermore, we propose a point corresponding method to implement the dynamic configuration. The aims are to minimize the over-all energy consumption in implementing configuration and balance the energy distribution among camera nodes, which results in the extension of network's lifetime. Simulation results highlight the proposed solution's quick-response feature with respect to the related heuristic algorithm, and exhibit the solution's potential to prolong network's lifetime.

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  Allen Y. Yang,et al.  CITRIC: A low-bandwidth wireless camera network platform , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[3]  Luca Benini,et al.  Tracking Motion Direction and Distance With Pyroelectric IR Sensors , 2010, IEEE Sensors Journal.

[4]  Luca Benini,et al.  Using a Wireless Sensor Network to Enhance Video Surveillance , 2007 .

[5]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[6]  Sufen Fong,et al.  MeshEye: A Hybrid-Resolution Smart Camera Mote for Applications in Distributed Intelligent Surveillance , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[7]  Nael B. Abu-Ghazaleh,et al.  Scalable target coverage in smart camera networks , 2010, ICDSC '10.

[8]  Boreom Lee,et al.  Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description , 2011, IEEE Transactions on Information Technology in Biomedicine.

[9]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[10]  Oliver Amft,et al.  A Distributed PIR-based Approach for Estimating People Count in Office Environments , 2012, 2012 IEEE 15th International Conference on Computational Science and Engineering.

[11]  Chengnian Long,et al.  Probability-based optimal coverage of PTZ camera networks , 2012, 2012 IEEE International Conference on Communications (ICC).

[12]  Chengnian Long,et al.  Optimal coverage of camera networks using PSO algorithm , 2011, 2011 4th International Congress on Image and Signal Processing.

[13]  Bernhard Rinner,et al.  Real-time video analysis on an embedded smart camera for traffic surveillance , 2004, Proceedings. RTAS 2004. 10th IEEE Real-Time and Embedded Technology and Applications Symposium, 2004..

[14]  Ying-Wen Bai,et al.  Enhancement of the sensing distance of an embedded surveillance system with video streaming recording triggered by an infrared sensor circuit , 2008, 2008 SICE Annual Conference.

[15]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[17]  R. Lienhart,et al.  On the optimal placement of multiple visual sensors , 2006, VSSN '06.

[18]  Diego López-de-Ipiña,et al.  Building an occupancy model from sensor networks in office environments , 2011, 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras.

[19]  A. Kak,et al.  A Look-up Table Based Approach for Solving the Camera Selection Problem in Large Camera Networks , 2006 .

[20]  Brett J. Borghetti,et al.  A Review of Anomaly Detection in Automated Surveillance , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[21]  Luiz Affonso Guedes,et al.  The Coverage Problem in Video-Based Wireless Sensor Networks: A Survey , 2010, Sensors.

[22]  Philip Brey,et al.  Freedom and Privacy in Ambient Intelligence , 2005, Ethics and Information Technology.

[23]  Bernhard Rinner,et al.  The evolution from single to pervasive smart cameras , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[24]  John Heidemann,et al.  Privacy-sensitive monitoring with a mix of ir sensors and cameras , 2003 .

[25]  Alhussein A. Abouzeid,et al.  Coverage by directional sensors in randomly deployed wireless sensor networks , 2006, J. Comb. Optim..

[26]  M. Moghavvemi,et al.  Pyroelectric infrared sensor for intruder detection , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[27]  Wolfgang Straßer,et al.  Adaptive Probabilistic Tracking Embedded in a Smart Camera , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[28]  Karl Aberer,et al.  Mining complex activities in the wild via a single smartphone accelerometer , 2012, SensorKDD '12.

[29]  Davide Anguita,et al.  Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.

[30]  Nael B. Abu-Ghazaleh,et al.  Coverage management for mobile targets in visual sensor networks , 2012, MSWiM '12.

[31]  Thomas Phan,et al.  An accurate two-tier classifier for efficient duty-cycling of smartphone activity recognition systems , 2012, PhoneSense '12.