CAP5415-Computer Vision Lecture 21-Deformable Part Model...

CAP5415-ComputerVisionLecture21-DeformablePartModel(DPM)

GuestLecturer:Sarfaraz Husein

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Motivation• Objectcategorydetection:

– Detectallobjectswiththesamecategoryinanimage• Forexamplehorsedetection:

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ClassificationTask

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Atleastonebus

100%

DetectionTask

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100%

Predictedboundingboxshouldoverlapbyatleast50%withgroundtruth!!!

Detections“nearmisses”

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PascalVOCChallenge-TheObjectClasses

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Imagesretrieved fromflickerwebsite.

SuccessfulDetectionMethod

• Jointwinnerin2009PascalVOCchallengewiththeOxfordMethod.

• Awardof"lifetimeachievement“in2010.• Mixtureofdeformablepartmodels.• Eachcomponenthasglobaltemplate+deformableparts– HOGfeaturetemplates.

• Fullytrainedfromboundingboxesalone.

(P.Felzenszwalbetal.)

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Recap:DeformableModels

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Challenge:handlingvariationofappearanceswithinobjectclassesnon-rigidobjects

Idea:Considerobjectsasadeformedversionofatemplate!

WhatisDPM?• Deformable Part is a discriminatively trained,

multi-scale model for image training that aim at making possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

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DPM• Definition:

– Root : Catch Roughly appearance of object– Part : Catch local appearance of object– Spring : spatial connections between parts

• Displacement :– Using minimizing energy function to find the optimal

displacement[1] Pictorial Structures for Object Recognition, Daniel P. Huttenlocher,1973

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Model

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(b1) coarse template (b2)part templates (b3) spatial model (a) person detection Example

The deformable model includes both a coarse global template covering an entire object and higher resolution part templates. The templates represent histogram of gradient features

ModelingSpatialRelationswithDPM

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Spring-based models

[Fischler & Elschlager ,’73], [Ramanan et al,’07], [Felszwenwalb et al,’05,’09], [Kumar et al, ‘09]

Parts Based Model

Vertices – Local Appearance

Edges - Spatial Relationship

JJ

JL

LL

Goal: Assign model parts to image regions preserving both local appearance and spatial

relationships

MatchingProblem

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MatchingProblem

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Parts based models - Inference Problem

Inference problem: What is the best scoring assignment f?

Local Appearance term Pairwise Spatial Relationship term

Inference is NP-hard for general graphs

For trees can use belief propagation for exact solution in polytime

Parts based models - Learning Problem

Linear models:

s.t.

Local Appearance term Pairwise Spatial Relationship term

Convex max-margin objective

Positive examples on one side

Negative examples on the other

[Kumar et al,’09]

Learning linear models: Find weight vectors that best separate positive and negative examples. E.g.,

FindingMotorbike

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HumanTracking

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HumanPoseEstimation

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HoG:HistogramofGradients

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HistogramofGradient1.Compute gradient every pixel2.Group 8*8 pixels into a cell, and 4*4 cells into a

block.Build histogram of each cells about 9 orientation

bins (00~1800)

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HistogramofGradient3.A block contain 4*9 feature vector for local

information, and a bounding box which contain n’s blocks has n*4*9 feature vector

4.Training by SVM

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Filters

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ObjectHypothesis

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Training

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LearnedModels

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Training Models• Latent SVM is used for learning the training images

Experiment Result• PASCAL VOC 2006,2007,2008 comp3 challenge datasets• Some statistics:

– It takes 2 seconds to evaluate a model in one image(4952 images in the test dataset)

– It takes 4 hours to train a model– MUCH faster than most systems.– All of the experiments were done on a 2.8Ghz 8-core Intel Xeon Mac

Pro computer running Mac OS X 10.5.

Experiment Result• Measurement: predicted bounding box is correct if it overlaps

more than 50 percent with ground truth bounding box; otherwise, considered false positive

Experiment Result

Experiment Result

Experiment Result• Best Average Precision score in 9 out of 20, second in 8

Experiment Result

SliceCreditsandReferences• JonathanHuang,TomaszMalisiewicz,2009.• P.Felzenszwalb,D.McAllester,D.Ramanan.ADiscriminatively Trained,

Multiscale,DeformablePartModel.IEEEConferenceonComputerVisionandPatternRecognition(CVPR),2008

• P.Felzenszwalb,R.Girshick,D.McAllester,D.Ramanan.ObjectDetectionwithDiscriminatively TrainedPartBasedModels. IEEETransactionsonPatternAnalysis andMachineIntelligence,Vol.32,No.9,Sep.2010.

• LevelImageRepresentationforSceneClassificationandSemanticFeatureSparsification.ProceedingsoftheNeuralInformationProcessingSystems(NIPS),2010.

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