1 / 23

AUTOMATIC TARGET RECOGNITION AND DATA FUSION

AUTOMATIC TARGET RECOGNITION AND DATA FUSION. March 9 th , 2004 Bala Lakshminarayanan. Outline. Introduction Distributed processing DSN topologies Data fusion SFTB project Classification results. Introduction. Automatic Target Recognition Classify civilian targets with high accuracy

dalton
Download Presentation

AUTOMATIC TARGET RECOGNITION AND DATA FUSION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. AUTOMATIC TARGET RECOGNITION AND DATA FUSION March 9th, 2004 Bala Lakshminarayanan

  2. Outline • Introduction • Distributed processing • DSN topologies • Data fusion • SFTB project • Classification results Bala Lakshminarayanan, 9th March

  3. Introduction • Automatic Target Recognition • Classify civilian targets with high accuracy • 7 targets • 3 sensors (IR, Grayscale, Acoustic) • 3 nodes • 3 scenarios • Nodes are placed along the road Bala Lakshminarayanan, 9th March

  4. Processing paradigms…(1) • Centralized processing – fusion center • High communication bandwidth • Higher network cost • Non-optimal processing, esp. when sensor coverage does not overlap • Central node dependence Bala Lakshminarayanan, 9th March

  5. Processing paradigms…(2) • Distributed processing • Redundancy – accurate classification • Lesser network cost • Reduced bandwidth requirement though increased communication between nodes • Better response to rapid changes • Needs proper architecture • Energy efficiency Bala Lakshminarayanan, 9th March

  6. Serial topology Event (H) yn y2 y1 u1 u2 un Sensor 1 Sensor 2 Sensor n yi : Local observation ui : Local decision variable n : Number of sensors u0 : Global decision variable Bala Lakshminarayanan, 9th March

  7. Parallel topology Event (H) yn y2 y1 Sensor 1 Sensor 2 Sensor n un u1 u2 • Classifier Selection model • Each classifier is an “expert” • For feature x, classifier in its vicinity is given highest credit Bala Lakshminarayanan, 9th March

  8. Parallel topology Event (H) yn y2 y1 Sensor 1 Sensor 2 Sensor n un u1 u2 • Classifier Fusion model • All classifiers trained over entire feature space • Competitive model Fusion Center u0 Bala Lakshminarayanan, 9th March

  9. Data fusion…(1) • Disparate sensors used for data collection • Need to integrate results – fuse sensor data to give user ability to decide better • Objective of fusion is to give one reliable, robust decision rather than many uncertain decisions • Fusion levels • Temporal • Multi-modal • Multi-sensor (from different nodes) Bala Lakshminarayanan, 9th March

  10. Data fusion…(2) • Temporal fusion • Independent frames • Majority voting • Multi-modality fusion • Different sensing modalities, all exposed to whole feature space • Competitive rather than complementary • BKS algorithm • Multi-sensor fusion • Handles faulty sensors • MRI algorithm Bala Lakshminarayanan, 9th March

  11. Data fusion…(3) Bala Lakshminarayanan, 9th March

  12. BKS Classification…(1) • Behaviour Knowledge Space • Aggregates results obtained from individual classifiers • Statistically, gives the optimal result • OCR on 46,451 numerals shows BKS outperforms voting, Bayesian and Dempster-Shafer • These approaches require the independence assumption – not so in real applications Bala Lakshminarayanan, 9th March

  13. BKS Classification…(2) • Independence assumption • All classifiers are assumed to be equal • Information for fusion is taken from confusion matrix of single classifier • BKS avoids the independence assumption by concurrently recording decisions of all classifiers • Behaviour of all classifiers recorded on a knowledge space - BKS Bala Lakshminarayanan, 9th March

  14. BKS Classification…(3) Feature Vector Classifier Class Label Crisp Classifier, Fuzzy classifier, Possibilistic classifier Decision can he hardened using the maximum membership rule Bala Lakshminarayanan, 9th March

  15. BKS Classification…(4) • Majority voting • Class labels are crisp or hardened • Crisp label most represented is assigned to x • Ties are broken randomly Bala Lakshminarayanan, 9th March

  16. BKS Classification…(5) • BKS • s1, s2, …, sL are crisp labels assigned to x by classifiers D1, D2, …, DL respectively • Every combination of labels is an index to an LUT Bala Lakshminarayanan, 9th March

  17. BKS Classification…(6) • Example of BKS • c = 3, L = 2, N = 100 Bala Lakshminarayanan, 9th March

  18. SFTB Framework Inputs-nodeID, scenario… Start Grab frames from dataset Continuous, non continuous Segment using bgSubtract() Background image Extract features invMoment() Normalize, write database files Classify readData(), knn() End Bala Lakshminarayanan, 9th March

  19. Results…(1) • 30 frames from each node – 5 testing, 25 training • 7 targets • 3 scenarios • 2 nodes (IR, Grayscale) • 6 databases with 210 feature vectors • 2 versions – database1, database2 Bala Lakshminarayanan, 9th March

  20. Results…(2) Node px12, Scenario 1, k=7, Classification accuracy 62.86% Node in23, Scenario 1, k=7, Classification accuracy 62.86% Bala Lakshminarayanan, 9th March

  21. Results…(3) Node PX12 Node IN23 Bala Lakshminarayanan, 9th March

  22. Future work • Perform fusion between IR and grayscale image data • Perform fusion between images from different scenarios Bala Lakshminarayanan, 9th March

  23. References • X. Wang, H. Qi, S. S. Iyengar, “Collaborative multi-modality target classification in distributed sensor networks”, International Conference on Information Fusion (ICIF), July 2002 • R. Viswanathan, P.K. Varshney, “Distributed Detection with multiple sensors: Part 1-Fundamentals”, Proceedings of the IEEE, Vol 85, Jan 1997 • L.I. Kuncheva, J.C. Bezdek, Robert P.W. Duin, “Decision templates for multiple classifier fusion: an experimental comparison”, Pattern Recognition, Vol 34, 2001 • Y.S. Huang, C.Y. Suen, “A method for combining multiple experts for the recognition of unconstrained handwritten numerals”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 17, Jan 1995 Bala Lakshminarayanan, 9th March

More Related