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Abnormal Object Detection by Canonical Scene -based Contextual Model

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.

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Abnormal Object Detection by Canonical Scene -based Contextual Model

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  1. Sangdon Park 2012.10.15. Abnormal Object Detection byCanonical Scene-based Contextual Model

  2. IntroductionProblem Statement Abnormal Object Detection (AOD) Input Output Which objects are abnormal?

  3. IntroductionProblem Statement Three types of Abnormal Objects Co-occurrence-violating abnormal object Position-violating abnormal object Scale-violating abnormal object

  4. IntroductionMotivation Increasing number of Abnormal Images Photoshop Artist Applicable to Visual Surveillance Duck Climbing

  5. IntroductionMotivation Limitation of the conventional method(1) NOT affluent object relations Tree-relation among objects quantitative object relations (1) M. J. Choi, A. Torralba, and A. S. Willsky, Context Models and Out-of-context Objects, To appear in Pattern Recognition Letters, 2012. affluent context types prior-free object search

  6. IntroductionContributions Solve new emerging problem • Abnormal Object Detection Novel latent Model • Generative model for AOD • Satisfies four conditions for AOD • Especially, affluent object relationships to strictly handle geometric context New abnormal dataset • object-level annotation

  7. Agenda Conventional Method Proposed Method Evaluations

  8. Conventional MethodTree-based model Tree-based Co-occurrence model Tree-based support model Efficient, but lack of relationship among object

  9. Proposed MethodOverall process

  10. Proposed MethodImage representation Object-level image representation “Undo” projectivity • Represent image by a set of bounding boxes that are extracted 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

  11. Proposed MethodMain Idea Identify abnormal ones! 11 • 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 Which object is abnormal? Define dist. of normal data & Compare? • Which object is less co-occur, floated/sunken, or big/small? • Compare the input with the distribution of normal objects • Check likelihood of input given the dist.

  12. Proposed MethodModel Define “Canonical Scene” • Natural distributions of normal objects • Less co-occurring objects does not exist • “Objects” are on the ground plane • Follows leaned truncated Gaussian distribution “Outdoor” CS

  13. Proposed MethodModel Define matching transformation & similarity measure • Matching transformation • T: 2D isometric transformation • Similarity measure

  14. Proposed MethodModel Model Return to the goal Decompose Appearance Model Location(Contextual) Model Prior model • Defined as conventional model • Defined by previous similarity measure • Prior on latent variables

  15. Proposed MethodModel Generative model Isometry Parameters of Canonical Scene

  16. Proposed MethodInference by Pop-MCMC Advantages of Pop-MCMC • Multiple Markov chains with genetic operations  escape from local optimum • Efficient when the objective function is multimodal and/or high dimensional

  17. Proposed MethodLearning Learning strategy • 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. Algorithm

  18. EvaluationNew Abnormal Dataset • Only abnormal objects are annotated • Scene types are also annotated

  19. EvaluationQuantitative comparisons • Proposed method(“red”) outperforms the baseline(“green”) 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.

  20. EvaluationQualitative comparisons • Because of affluent object relation, floating person is detected as most abnormal objects

  21. EvaluationQualitative results • Only top-5 most abnormal objects are represented

  22. Conclusion Novel Model for Abnormal Object Detection • 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 Limitations

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