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Object Localization by Efficient Subwindow Search

Object Localization by Efficient Subwindow Search. JOJO 2010.12.9. Outline. Background Paper Research background Sliding Window Approach Efficient Subwindow Search (ESS) Branch-and-Bound Search Application Experiment (Car Detection) Data & Algorithm Experiment design Conclusion.

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Object Localization by Efficient Subwindow Search

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  1. Object Localization by Efficient Subwindow Search JOJO 2010.12.9

  2. Outline • Background • Paper • Research background • Sliding Window Approach • Efficient Subwindow Search (ESS) • Branch-and-Bound Search • Application • Experiment (Car Detection) • Data & Algorithm • Experiment design • Conclusion

  3. Background (Paper) • Title: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search • Author: Christoph H. Lampert • Source: IEEE International Conference on Computer Vision and Pattern Recogniton,2008

  4. Background (Research background) • Object recognition systems: only decide if the object is present or not not provide the actual object location. • Sliding window approach (previous localiza-tion method): Computational cost is seriously expensive

  5. Outline • Background • Paper • Research background • Sliding Window Approach • Efficient Subwindow Search (ESS) • Branch-and-Bound Search • Application • Experiment (Car Detection) • Data & Algorithm • Experiment design • Conclusion

  6. Sliding Window Approach • Target: Find a bounding box around the object • Tool: Quality function • Perform:

  7. Sliding Window Approach • All rectangular regions • The image • Quality function

  8. Sliding Window Approach • Time Complexity: • must be smooth and slowly varying • Local optimally ESS

  9. Outline • Background • Paper • Research background • Sliding Window Approach • Efficient Subwindow Search (ESS) • Branch-and-Bound Search • Application • Experiment (Car Detection) • Data & Algorithm • Experiment design • Conclusion

  10. ESS (Branch-and-Bound Search)

  11. ESS (Branch-and-Bound Search)

  12. ESS (Branch-and-Bound Search) Set of rectangles

  13. ESS (Application) • Localization of non-rigid objects (animals) • Localization of rigid objects (car) • Image part retrieval

  14. Outline • Background • Paper • Research background • Sliding Window Approach • Efficient Subwindow Search (ESS) • Branch-and-Bound Search • Application • Experiment (Car Detection) • Data & Algorithm • Experiment design • Conclusion

  15. Experiment (Car Detection) • Data UIUC Car dataset • Algorithm

  16. Experiment (Car Detection)

  17. Experiment (Car Detection) 1 SVM： • Linear kernel • Feature: Grid_based SURF extracted the SURF features from regular grids of size 4x4 to 40x40. • Hierarchical spatial pyramid histograms Speeded up robust feature

  18. Experiment (Car Detection) SVM： • Hierarchical spatial pyramid histograms

  19. Experiment (Car Detection) 1 SVM： • Hierarchical spatial pyramid histograms The number of pyramid levels vary between 1 and 10

  20. Experiment (Car Detection) 2 Decision function：

  21. Experiment (Car Detection) 3 Upper bound function ：

  22. Experiment (Car Detection) Experiment design Error rates on UIUC Cars dataset at the point of equal precision and recall.

  23. Outline • Background • Paper • Research background • Sliding Window Approach • Efficient Subwindow Search (ESS) • Branch-and-Bound Search • Application • Experiment (Car Detection) • Data & Algorithm • Experiment design • Conclusion

  24. Conclusion • Advantage： fast global optimally better local classifier (SVM) more choices of f • Future work： other kernel-based classifier other parametric shape (circle, ellipse)

  25. Thank you！

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