Supervisor: Nakhmani ArieSupervisor: Nakhmani Arie

Semester: Winter 2007Semester: Winter 2007

Target Target RecognitionRecognition

Harmatz IscaHarmatz Isca

Project goalsProject goals

Create a target classification system Create a target classification system based on dimension reduction, using the based on dimension reduction, using the targets contour.targets contour.

No dependence on illumination and colorNo dependence on illumination and colorUniversal method works on all target types Universal method works on all target types and sizesand sizesFast learning for new targetsFast learning for new targetsLow computational needsLow computational needsThe dimension reduction algorithm can be The dimension reduction algorithm can be adopted to work on all types of data.adopted to work on all types of data.

Motivation Motivation

Tracking people Tracking people

ATR– automatic target recognitionATR– automatic target recognition

Find suspects in given areasFind suspects in given areas

Look for specific characteristics of targetsLook for specific characteristics of targets

MethodMethod

ResultResult

Post processing

Post processing

DimensionReduction

DimensionReduction

SnakesSnakes

Change Detection

Change Detection

Working DatabaseWorking Database 475 images475 images

2176 snakes found2176 snakes found

The snakes were divided into 3 types: The snakes were divided into 3 types: Real Real (339) – a snake of a person (339) – a snake of a person

PartialPartial (155) – a snake were the person was (155) – a snake were the person was partially hidden, or a clear silhouette was not partially hidden, or a clear silhouette was not detecteddetected

FalseFalse (1682) – a snake of a random change in (1682) – a snake of a random change in the imagethe image

Get several reference images

Create average reference image

= Background Image

Subtract the background from the imageFind changed pixels

Change detectionChange detection

Detect changes in image

SnakesSnakesLevel Set Evolution Without Re-initialization: A New Variational FormulationLevel Set Evolution Without Re-initialization: A New Variational FormulationChunming Li, Chenyang Xu, Changfeng Gui, and Martin D. FoxChunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox

CVPR 2005CVPR 2005

Dimension reductionDimension reduction

• Select target snake

• Transform snake to vector

• Add snake vector to vector database

• Perform dimension reduction on vectors

• Displaying dimension reduction results in graph

15222540467073292010

14182133453025201615

X Y 15222540467073292010

14182133453025201615

Database25583696467124462881

20247782671326693214

41255859536794313776

19739713642879148239

96325963155756954273

74032426232181894656

58143256874628818357

74582514369545672132

85143695748224654681

74853569211425496758

54688945213425194776

10232540467071252011

LLE or PCALLE or PCA

2154

7425

8451

8653

7128

2526

8239

7414

1649

8234

4986

4362

4825

5314

For every snake in database:For every snake in database:Find K nearest neighbors { zFind K nearest neighbors { z1:K 1:K }}

Find weight WFind weight Wijij for every neighbor z for every neighbor z jj

Compute the projection to lower space where Compute the projection to lower space where weighted distance from neighbors is minimumweighted distance from neighbors is minimum

2n

i iji=1 1

ij i ij j i1

minimizing W n -size of database

s.t. W 1, ; W 0 if z is not a neighbor of z

K

ijj

n

j

z z

z

Local Linear Embedding Local Linear Embedding (LLE)(LLE)

Principal components Principal components analysis (PCA)analysis (PCA)

Calculate the covariance matrix of databaseCalculate the covariance matrix of database

Calculate eigenvectors (ordered by eigenvalues)Calculate eigenvectors (ordered by eigenvalues)

Find snakes representation with eigenvectorsFind snakes representation with eigenvectors

0.51

0.12

0.3 0.07

+ + +

LLE vs PCALLE vs PCA

LLELLE

Non-linear embeddingNon-linear embedding

LocalLocal

Keeps subspace with Keeps subspace with best local linear structurebest local linear structure

Assumes local linearityAssumes local linearity

PCAPCA

Linear embeddingLinear embedding

GlobalGlobal

Keeps subspace with Keeps subspace with best variance of databest variance of data

Assumes global linearityAssumes global linearity

Results LLEResults LLE

2

( , )

Database

d Snake DatabaseGrade

Results PCAResults PCA

2

( , )

Database

d Snake DatabaseGrade

Post-processing Post-processing

Steps taken to achieve better separation Steps taken to achieve better separation between false and true snakesbetween false and true snakes

Compactness: Area/Perimeter²Compactness: Area/Perimeter²

Adaptive DatabaseAdaptive Database

Target TrackingTarget Tracking

CompactnessCompactness

Grade = area/perimeter2

Dimension Reduction and Dimension Reduction and CompactnessCompactness

Grade = GradePCA .

GradeCompactness

Adaptive DatabaseAdaptive Database

UnsupervisedUnsupervisedSnakes matching a certain grade level are Snakes matching a certain grade level are added to the database. Snakes in database added to the database. Snakes in database with low grades are removed.with low grades are removed.

The algorithm was applied for every movie The algorithm was applied for every movie separatelyseparately

Adaptive DatabaseAdaptive Database

2

( , )

Database

d Snake DatabaseGrade

TrackingTracking

Define Target of interestDefine Target of interest

For every next image:For every next image:Define search regionDefine search region

If “good” snake is found, thenIf “good” snake is found, then Set target to found snakeSet target to found snake

ElseElse Increase search area Increase search area

Move to next imageMove to next image

Tracking ResultsTracking Results

ConclusionsConclusions

Dimension reduction was used to find Dimension reduction was used to find people in images.people in images.The method works well on clear The method works well on clear silhouettes.silhouettes.Different post-processing methods used to Different post-processing methods used to improve results, each with its own pros improve results, each with its own pros and cons.and cons.The method works with a small database The method works with a small database (20 snakes) and can be adopted for real (20 snakes) and can be adopted for real time work.time work.

Feature Directions Feature Directions

Occluded target supportOccluded target support

Improve target trackingImprove target trackingMultiple targetsMultiple targets

Kalman / Particle filtersKalman / Particle filters

Target specific databaseTarget specific database

Adaptive grade thresholdAdaptive grade threshold

Improved snakesImproved snakes