Difference between revisions of "SCN"

From Aicip
Jump to: navigation, search
(Graduated Students)
(Graduated Students)
Line 32: Line 32:
 
* NSF CNS-1017156
 
* NSF CNS-1017156
 
   
 
   
== Graduated Students ==
+
== Students ==
  
 
* Jiajia Luo, Ph.D. Candidate, Started Fall 2009
 
* Jiajia Luo, Ph.D. Candidate, Started Fall 2009
  
* Mahmut Karakaya, Ph.D. Candidate, Expected Summer 2011
+
* Kefa Lu, Ph.D. Candidate, Started Fall 2010
 +
 
 +
* Zhibo Wang, Ph.D. Candidate, Started Spring 2011
 +
 
 +
* Mahmut Karakaya, Collaborative Solutions to Visual Sensor Networks, Ph.D., Summer 2011
  
 
* Yang Bai, Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks, Ph.D., Spring 2011
 
* Yang Bai, Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks, Ph.D., Spring 2011
  
* Kefa Lu, Ph.D. Candidate, Started Fall 2010
 
 
   
 
   
 
<br>
 
<br>

Revision as of 15:17, 28 June 2011

Project Summary

Smart camera networks (SCNs) merge computer vision, distributed processing, and sensor network disciplines to solve problems in multi-camera applications by providing valuable information through distributed sensing and collaborative in-network processing. Collaboration in sensor networks is necessary not only to compensate for the processing, sensing, energy, and bandwidth limitations of each sensor node but also to improve the accuracy and robustness of the network. Collaborative processing in SCNs is more challenging than in conventional scalar sensor networks (SSNs) because of three unique features of cameras, including the extremely higher data rate, the directional sensing characteristics with limited field of view (FOV), and the existence of visual occlusion. An integrated research is carried out to tackle the unique challenges presented by SCNs where collaboration is the key. Three aspects of collaborative processing are investigated, 1) coverage estimation‚ in the presence of visual occlusions to provide adequate redundancy in sensing coverage to enable collaboration where the statistics of visual coverage blends the statistics of camera nodes and targets, 2) clustering‚ to schedule an efficient sleep-wakeup pattern among neighbor nodes formed by image comparison-based semantic neighbor selection algorithm for more efficient collaboration, and 3) distributed optimization‚ for in-network data processing that concerns how to effectively obtain robust and accurate integration results from multiple distributed sensors for challenging vision tasks like target detection, localization, and tracking in crowds.

Sponsors

Logo-nsf.jpg

  • NSF CNS-1017156

Students

  • Jiajia Luo, Ph.D. Candidate, Started Fall 2009
  • Kefa Lu, Ph.D. Candidate, Started Fall 2010
  • Zhibo Wang, Ph.D. Candidate, Started Spring 2011
  • Mahmut Karakaya, Collaborative Solutions to Visual Sensor Networks, Ph.D., Summer 2011
  • Yang Bai, Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks, Ph.D., Spring 2011



  • Cheng Qian, A Distributed Solution for Visual Sensor Networks to Detect Targets in Crowds, M.S., Summer 2006
  • Chris Beall, Distributed Self-Deployment in Visual Sensor Networks, M.S., Summer 2006

Publications

Journal Publications

  • Y. Bai, H. Qi, "Feature-based image comparison for semantic neighbor selection in resource-constrained visual sensor networks," Eurasip Journal on Image and Video Processing (IVP), vol. 2010, Article ID 469563, 11 pages, 2010. doi: 10.1155/2010/469563.


Conference Papers

  • Q. Cao, X. Wang, H. Qi, T. He, "r-Kernel: An operating system foundation for highly reliable networked embedded systems," The 30th International Conference on Computer Communications (INFOCOM), Shanghai, China, April 10-15, 2011.
  • M. Karakaya, H.Qi, "Fault Detection, Correction, and Tolerance for Collaborative Target Localization in Visual Sensor Networks" 4th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Atlanta, GA, Aug 30-Sep 2, 2010.
  • J. Luo, H. Qi, "Distributed object recognition via feature unmixing," ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), 8 pages, Atlanta, GA, August 31 - September 4, 2010.
  • M. Karakaya, H.Qi, "Target Detection and Counting using a Progressive Certainty Map in Distributed Visual Sensor Networks" 3rd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Como, Italy, Aug 30-Sep 2, 2009. (Best Paper Award)
  • Y. Bai, H. Qi, "Redundancy removal through semantic neighbor selection in visual sensor networks," Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), 8 pages, Como, Italy, August 30 - September 2, 2009.
  • C. Qian, H. Qi, "A distributed solution to detect targets in crowds using visual sensor networks" 2nd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Stanford University, CA, September 7-11, 2008.
  • C. Qian, H. Qi, "Coverage estimation in the presence of occlusions for visual sensor networks," International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, June 11-14, 2008.
  • C. Beall, H. Qi, "Distributed self-deployment in visual sensor networks" Ninth International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, December 5-8, 2006.