1. 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 ICIP 2012

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

5. Sensing Model Sending Model:

6. 1 2 3 4 5 4 1.5 3.5 2.5 … 3... 3 Average Consensus: Review Average Consensus Algorithm Example of Average Consensus Each nodes converges to the global average 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 measurements at 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? Weighted-average consensus Converges to the optimal ML estimate (not affected by network sparsity.)

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

11. Conclusion This work was partially supported by ONR award N00014091066 titled Distributed Dynamic Scene Analysis in a Self-Configuring Multimodal Sensor Network. 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”)

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