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Consensus-based Distributed Estimation in Camera Networks

ICIP 2012. Consensus-based Distributed Estimation in Camera Networks. - A. T. Kamal, J. A. Farrell, A. K. Roy- Chowdhury University of California, Riverside -akamal@ee.ucr.edu. Contents. Problem Statement Motivation for using Distributed Schemes

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Consensus-based Distributed Estimation in Camera Networks

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  1. ICIP 2012 Consensus-based Distributed Estimation in Camera Networks - A. T. Kamal, J. A. Farrell, A. K. Roy-Chowdhury University of California, Riverside -akamal@ee.ucr.edu

  2. Contents • Problem Statement • Motivation for using Distributed Schemes • Challenges in Distributed Estimation in Camera Networks • Our solution • Results

  3. Problem Statement • Our goal is to estimate the state of the targets using the observations from all the cameras in a distributed manner.

  4. Motivation for using Distributed Schemes Network architectures for multi-camera fusion Partially connected Centralized Fully connected • Issues using centralized or fully connected architectures: • High communication & processing power requirements. • Intolerant of node failure. • Complicated to install. • Distributed schemes are scalable for any given connected network

  5. Sensing Model Parameter Vector: can be position, pose, appearance feature etc. of a target , Sending Model:

  6. Average Consensus: Review … 3 1.5 3 • Average Consensus Algorithm 1 4 … 3 • Each nodes converges to the global average 5 2.5 ... 3 2 3.5 … 3 4 3.5 … 3 Example of Average Consensus R. Olfati-saber, J. A. Fax, and R. J. Murray, “Consensus and cooperation in networked multi-agent systems,” in Proceedings of the IEEE, 2007

  7. Challenges in Distributed Estimation in Camera Networks • Challenges: • Each node may not observe the target (i.e. difference between vision graph and comm. graph) • The quality (noise variance) of measurementsat different nodes may be different. • Network sparsity makes the above challenges severe. • We propose a distributed estimation framework which: • Does not require the knowledge of the vision graph. • Weights measurements by noise variances. • Network sparsity does not affect the estimate it converges to.

  8. Distributed Maximum Likelihood Estimation (DMLE) Information Matrix Weighted Measurement

  9. How is does DMLE solve the challenges? • Presence/absence and quality of measurement is captured in .(, for no node measurement) • Weighted-average consensus • Converges to the optimal ML estimate(not affected by network sparsity.)

  10. Experimental Evaluation Legend: Error Statistics Ground Truth Observations Avg. Consensus DMLE * *

  11. Conclusion • We have proposed a distributed parameter estimation method generalized for • Limited observability of nodes • Variable quality of measurements and • Network sparsity • that approaches the performance of the optimal centralized MLE. • Future Work: Dynamic State Estimation (Distributed Kalman Filtering) • Incorporation of prior information and state dynamics (“Information Weighted Consensus - IEEE Decision and Control Conference, Dec 2012”) This work was partially supported by ONR award N00014091066 titled Distributed Dynamic Scene Analysis in a Self-Configuring Multimodal Sensor Network.

  12. Thank you • For more information and recent works please visit: http://www.ee.ucr.edu/~akamal/

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