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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