AUTOMATIC TARGET RECOGNITION OF CIVILIAN TARGETS September 28 th, 2004 Bala Lakshminarayanan

Outline Objective Introduction to ATR Details of SFTB Database creation Segmentation Feature extraction, classification Results Conclusions

Objective Civilian target classification Sensor fusion SFTB objectives - Generation of dataset for ATR - Ground truth data collection

Introduction to ATR What is ATR Why do we need it Types of ATR - Aided, unaided - Binary, multi-valued Problems

Introduction to ATR Requirements - Real time operation - Low false positives - High detection rates Applications - Military - Medical - Industrial

SFTB Nodes - Base station - 2 with IR sensor - 1 with visible light sensor Node placement Targets (cars, light trucks, SUVs) Ground truth collection equipment Scenarios

SFTB Image provided by Night vision lab

SFTB Fully exposed targets except by other presence on scene Stationary sensors Daylight operation License plates not readable Constant velocity/acceleration Different scenarios (3) Simultaneous data capture

Images Node 1 Node 3 Node 2

Project Objective Use IR and visual images to classify targets Use sensor fusion to improve accuracy Creation of image database Creation of framework Segmentation, feature extraction, classification

Database Creation Images in.arf files Use frames captured at same time “Event start” - Range from Node2 = 20 “Event end” - Outside FoV of Node3

Framework Start Grab frame from dataset filename() Segment bgSubtract(), motionDet() Extract features invMoment() Classify readData(), knn() End Inputs-nodeID, scenario…

Segmentation Used to identify the target/RoI in the frame Methods - Thresholding - Background subtraction - Motion based segmentation

Segmentation Background subtraction median(frame)-median(background) Noise removal by neighbourhood() -= - =

Segmentation Motion based segmentation temp1=average(prev)-average(frame) temp2=average(next)-average(frame) temp1&temp2

Feature Extraction Features should describe similar targets similarly Seven invariant moments (Hu, 1962) Computed from central moments, third order Translational invariance – C.G Distance invariance – Size normalization

Feature Extraction  1 =  20 +  02,  2 = (  20 -  02 )  2 11  3 = (   12 ) 2 + (   21 ) 2,  4 = (  30 +  12 ) 2 + (  03 +  21 ) 2  5 = (3   12 )(  30 +  12 )[(  30 +  12 ) 2 –3(  21 +  03 ) 2 ] + (3  21 -  03 )(  21 +  03 )  [3(  30 +  12 ) 2 – (  21 +  03 ) 2 ]  6 = (  20 -  02 )[(  30 +  12 ) 2 – (  21 +  03 ) 2 ] + 4  11 (  30 +  12 )(  21 +  03 )  7 = (3  21 -  03 )(  30 +  12 )[(  30 +  12 ) 2 - 3(  21 +  03 ) 2 ] + (3  12 -  30 )(  21 +  03 )  [3(  30 +  12 ) 2 – (  21 +  30 ) 2 ] Central moments Normalized moments

Classification Supervised or unsupervised k-nearest neighbour method Training vectors are given Find k nearest neighbours, maximum presence

Results 3 classes - 1, 2, 4; single scenario 7 features - 5 training vectors, 2 testing vectors k = 1, 3 Class K= K= Class K= K=

Results Overall classification results k=1 – 58.33% k=3 – 50% Target1 – 25% Target2 – 38.5% Target4 – 100%

Results Confusion matrix k= k=

Conclusions Database created Basic framework has been laid Robust segmentation needed More training vectors Segmentation does not work for px files

Future work Segmentation - Quadtree based split-merge - Use of Kalman filters - Histogram based segmentation Better features need to be used

Thanks ?? and !!