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Combined shape and feature-based video analysis and its application to non-rigid object tracking

Combined shape and feature-based video analysis and its application to non-rigid object tracking. 資訊碩一 10077034 蔡勇儀 2011/11/01 @LAB603. Outline. Introduction Method Background generation and updating Detection of moving object Shape control points

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Combined shape and feature-based video analysis and its application to non-rigid object tracking

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  1. Combined shape and feature-based video analysis and its application to non-rigid object tracking 資訊碩一 10077034 蔡勇儀 2011/11/01 @LAB603

  2. Outline • Introduction • Method • Background generation and updating • Detection of moving object • Shape control points • Combined shape and feature-based object tracking • Object occlusion • Result • Conclusions

  3. Introduction – object motion • Object motion detect is an important issue of computer vision. • Many challenges • Complex background • More object motion • Occlusion • Illumination change • Dynamic shading • Camera jitter • …

  4. Introduction – methods(1/2) • Active shape model(ASM) • Pre-model object’s shape • Priori trained shape information • Manually determined landmark point • Can’t real time • Non-prior training active feature model(NPT-AFM) • Consider feature point without object shape • Improve computational efficiency • Doesn’t utilise background information

  5. Introduction – methods(2/2) • Block matching algorithm(BMA) • Block matching between two frame • Direct matching nature simplifies motion • Preserves object’s feature which can’t be easily parameterized • Poor performance with non-rigid shapes and similar patterns to the background.

  6. Introduction – This paper method • Background generation • Motion detection and SCP extraction • Object shape tracking modules

  7. Outline • Introduction • Method • Background generation and updating • Detection of moving object • Shape control points • Combined shape and feature-based object tracking • Result • Conclusions

  8. Method – Background generation • Use median filter & BMA • Define sum of absolute difference(SAD) and threshold(0.05) • Find background(Static)

  9. Method – Detection of moving object

  10. Method – Shape control points(1/2) • Find feasible boundary R represents the minimum rectangular box enclosing the object.

  11. Method – Shape control points(2/2) • Build SCP set • K: interval of skipping redundant SCPs

  12. Method - Combined shape and feature-based object tracking • Get block SCP • If object deformation, occlusion(25%)… • CBMA – computing distances among SCPs • PBMA – fix motion region

  13. Method - Summary

  14. Outline • Introduction • Method • Background generation and updating • Detection of moving object • Shape control points • Combined shape and feature-based object tracking • Result • Conclusions

  15. Result(1/2)

  16. Result(2/2)

  17. Outline • Introduction • Method • Background generation and updating • Detection of moving object • Shape control points • Combined shape and feature-based object tracking • Result • Conclusions

  18. Conclusions • BMA & CBMA • The number of SCPs • Optimal region(feature histogram)

  19. Thanks for your listening • Source： IET Image Process, 2011, Vol.5, Iss.1, pp.87-100

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