AUTOMATIC TARGET RECOGNITION AND DATA FUSION

AUTOMATIC TARGET RECOGNITION AND DATA FUSION

March 9th, 2004

Bala Lakshminarayanan

Bala Lakshminarayanan, 9th March 2

Outline

Introduction Distributed processing DSN topologies Data fusion SFTB project Classification results


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


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


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


Serial topology

Event (H)

Sensor 1 Sensor 2 Sensor n

y1y2 yn

u1 u2 un

yi : Local observation

ui : Local decision variable

n : Number of sensors

u0 : Global decision variable


Parallel topology

Event (H)


y1y2 yn

u1 u2 un

• Classifier Selection model

• Each classifier is an “expert”

• For feature x, classifier in its vicinity is given highest credit


Parallel topology

Event (H)


y1y2 yn

u1 u2 un

Fusion Centeru0

• Classifier Fusion model

• All classifiers trained over entire feature space

• Competitive model


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)


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


Data fusion…(3)

Multi sensor fusion(From different

nodes)

Multi-modality fusion(From different

sensing modalities)

Multi-modality fusion(From different

sensing modalities)

Temporal fusion(From different

frames)


frames)


frames)


frames)

GRAYSCALE NODE(Frame1, frame2,

Frame3….frameN)

IR NODE(Frame1, frame2,

Frame3….frameN)


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



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



x R n

D R n c: [ , ] { } 0 1 0

M xD ( )

Feature Vector

Classifier

Class Label

Crisp Classifier, Fuzzy classifier, Possibilistic classifier

Decision can he hardened using the maximum membership rule

D x k M x M x iDK

Di( ) ( ) m ax ( ( ))



Majority voting Class labels are crisp or hardened Crisp label most represented is assigned to x Ties are broken randomly



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


BKS Classification…(6) Example of BKS c = 3, L = 2, N = 100

s1, s2 Number from each class

Label

1,1 10/3/3 1

1,2 3/0/6 3

1,3 5/4/5 1,3

2,1 0/0/0 0

2,2 1/16/6 2

2,3 4/4/4 1,2,3

3,1 7/2/4 1

3,2 0/2/5 3

3,3 0/0/6 3


SFTB Framework Start

Grab frames from datasetContinuous, non continuous

Segment using bgSubtract()Background image

Extract features invMoment()Normalize, write database files

ClassifyreadData(), knn()

End

Inputs-nodeID, scenario…


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


Results…(2)

Node px12, Scenario 1, k=7, Classification accuracy 62.86%

Targets 1 2 3 4 5 6 7

1 4 - - - - - 1

2 - 5 - - - - -

3 - - 4 - - 1 -

4 - - - 5 - - -

5 - - - - 1 - 4

6 - 2 2 - - 1 -

7 3 - - - - - 2

Targets 1 2 3 4 5 6 7

1 3 - 1 - 1 - -

2 - 5 - - - - -

3 1 - 4 - - - -

4 - - 2 2 1 - -

5 - - - - 4 - 1

6 1 1 - - - 3 -

7 - - - - - 2 3

Node in23, Scenario 1, k=7, Classification accuracy 62.86%


Results…(3)

Scenario k=7 k=9 k=11

1 62.86 68.57 68.57

6 68.57 68.57 71.43

25 88.57 88.57 80.00

Scenario k=7 k=9 k=11

1 62.86 45.71 51.43

6 57.14 54.28 57.14

25 48.57 48.57 34.28

Node PX12

Node IN23


Future work

Perform fusion between IR and grayscale image data

Perform fusion between images from different scenarios


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

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AUTOMATIC TARGET RECOGNITION AND DATA FUSION