Sangdon Park 2012.10.15.. 2 Which objects are abnormal ? InputOutput Abnormal Object Detection (AOD)
Transcript of Sangdon Park 2012.10.15.. 2 Which objects are abnormal ? InputOutput Abnormal Object Detection (AOD)
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Introduction
Problem Statement
Which objects are
abnor-mal?
Input Output
Abnormal Object Detection (AOD)
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Introduction
Problem Statement
Position-violating abnormal object
Co-occurrence-vio-lating
abnormal object
Scale-violating abnormal object
Three types of Abnormal Objects
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Introduction
Motivation
Photo-shop
Artist
Duck Climbing
Increasing number of Abnormal Images
Applicable to Visual Surveillance
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Introduction
Motivation
NOT affluent object re-lations
quantitative object re-lations
affluent context typesprior-free object
search
(1) M. J. Choi, A. Torralba, and A. S. Willsky, Context Models and Out-of-context Ob-
jects, To appear in Pattern Recognition Let-ters, 2012.
Tree-relation among ob-jects
Limitation of the conventional method(1)
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Introduction
Contributions
Abnormal Object Detection
object-level annotation
Generative model for AOD Satisfies four conditions for AOD
Especially, affluent object relationships to strictly handle geometric context
Solve new emerging problem
Novel latent Model
New abnormal dataset
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Conventional Method
Tree-based model
Tree-basedCo-occurrence
model
Tree-basedsupport model
Efficient, but lack of relationship among ob-ject
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Proposed Method
Image representation
Represent image by a set of bounding boxes that are ex-tracted by object detectors
Each image consists of bounding boxes (=100, in this paper)
Transform “image coordinate” to “camera coordinate” by simple triangulation
Represent position and scale information altogether
Object-level image represen-tation
“Undo” projectivity
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Proposed Method
Main Idea
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Which object is ab-normal?
Which object is less co-occur, floated/sunken, or big/small?
Define dist. of normal data & Com-pare?
Compare the input with the distribution of normal objects
Check likelihood of input given the dist.
Identify abnormal ones!
How to represent the distribution of normal scene? Construct the Canonical Scene (CS) model How to compare the input scene with the normal scene? Matching transformation T for CS Similarity measure to compare the input scene and transformed CS
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Proposed Method
Model
Define “Canonical Scene”
Natural distributions of normal objects
Less co-occurring objects does not exist
“Objects” are on the ground plane
Follows leaned truncated Gauss-ian distribution
“Outdoor” CS
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Proposed Method
Model
Define matching transformation & similar-ity measure
Matching transformation T: 2D isometric transformation
Similarity measure ),,|,(),( ,,,,,, ,
TlsxKpxLm nononononoTls no
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Proposed Method
Model
Return to the goal
Appearance Model
)|( cyp
Defined as conven-tional model
Model
Decom-pose
Location(Contextual) ModelKlxK d),,|,( Tsp
Defined by previous similarity measure
Prior model
),,,( Tsp cl
Prior on latent variables
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Proposed Method
Inference by Pop-MCMC
Advantages of Pop-MCMC
Multiple Markov chains with genetic opera-tions
escape from local optimum Efficient when the objective function is multi-
modal and/or high dimensional
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Proposed Method
Learning
Estimate T, thus making complete data Assumes all “objects” in normal images are on the
ground plane T is a transformation that transform ground plane in
world coord. to slanted plane in camera coord.1T
Learning strat-egy
Algorithm
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Evaluation
New Abnormal Dataset
#images 149
#Co-occur-rence
38
#Position 53
#Scale 44
#mixed 14
Only abnormal objects are annotated
Scene types are also anno-tated
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Evaluation
Quantitative comparisons
CO+SUP: M. J. Choi, A. Torralba, and A. S. Willsky, Context Models and Out-of-context Objects, To appear in Pattern Recognition Letters, 2012.
Proposed method(“red”) outperforms the baseline(“green”)
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Evaluation
Qualitative comparisons Because of af-
fluent object relation, float-ing person is detected as most abnor-mal objects
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Conclusion
Learning Full parameter learning is required Annotation errors Cannot estimate ground
plane strictly poor performance on detecting scale-violating abnormal objects
New abnormal dataset Generative model Satisfies four conditions for AOD
Especially, affluent object relationships to strictly handle geometric context
State-of-the-art performance
Novel Model for Abnormal Object Detection
Limitations