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Sangdon Park 2012.10.15. Abnormal Object Detection by Canonical Scene -based Contextual Model. Introduction Problem Statement. Abnormal Object Detection (AOD). Input. Output. Which objects are abnormal ?. Introduction Problem Statement. Three types of Abnormal Objects. - PowerPoint PPT Presentation
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Abnormal Object Detection byCanonical Scene-based Contextual Model
Sangdon Park2012.10.15.
2
Introduction
Problem Statement
Which objects are abnor-mal?
Input Output
Abnormal Object Detection (AOD)
3
Introduction
Problem Statement
Position-violating abnormal object
Co-occurrence-vio-lating
abnormal objectScale-violating abnormal object
Three types of Abnormal Objects
4
Introduction
Motivation
Photo-shop
Artist
Duck Climbing
Increasing number of Abnormal Images
Applicable to Visual Surveillance
5
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)
6
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
7
Agenda
Conventional Method
Proposed Method
Evaluations
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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
Overall process
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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
11
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
13
Proposed Method
ModelDefine matching transformation & similar-
ity measure
Matching transformation T: 2D isometric transformation
Similarity measure ),,|,(),( ,,,,,, ,
TlsxKpxLm nononononoTls no
14
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 la-tent variables
15
Proposed Method
Model
Parameters of Canonical
Scene
Isometry
Generative model
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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 an-notated
Scene types are also anno-tated
19
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”)
20
Evaluation
Qualitative comparisons Because of
affluent ob-ject relation, floating per-son is de-tected as most abnor-mal objects
21
Evaluation
Qualitative results
Only top-5 most abnormal objects are represented
22
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 han-dle geometric context
State-of-the-art performance
Novel Model for Abnormal Object Detection
Limitations