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Example-Based Object Detection in Images by Components

Example-Based Object Detection in Images by Components. Mohan, Papageorgious and Poggio IEEE PAMI 2001 Presented by Jiun-Hung Chen April 11, 2005. Summary. Goal: Detect objects in static images How: Exampled-based person detection framework by components

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Example-Based Object Detection in Images by Components

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  1. Example-Based Object Detection in Images by Components Mohan, Papageorgious and Poggio IEEE PAMI 2001 Presented by Jiun-Hung Chen April 11, 2005

  2. Summary • Goal: Detect objects in static images • How: Exampled-based person detection framework by components • Heads, legs, left arms and right arms detectors • Components are present in the proper geometric configuration • Person detector combines the results of the component detectors for person detection • Adaptive combination of classifiers (ACC) • Harr wavelet transform + support vector machines (SVM) • Significantly better than a similar full-body person detector

  3. Previous Work • Object detection • Model-based • Image invariance • Example-based • Classifier combination algorithms • Bagging, Boosting, Voting and so on

  4. Challenges in Person Detection • Nonrigid objects, colors, garment types • Rotated in depth, partially occluded or in motion

  5. System Diagram

  6. Geometric Constraints

  7. Harr Wavelet Transform From www.matlab.com

  8. Support Vector Machines (SVM) • First, project input data nonlinearly and implicitly by kernel functions to a feature space • Mercer’s kernels (Polynomial kernels and Gaussian radial basis function kernels) • Second, find optimal decision hyperplane in the feature space by maximizing soft margins and an upper bound of training errors • The raw output of an SVM classifier is the distance of a data point from the decision hyperplane

  9. Training Examples

  10. Experimental Results

  11. Experimental Results (Cont.)

  12. Experimental Results (Cont.)

  13. Experimental Results (Cont.)

  14. Experimental Results (Cont.)

  15. Learned Geometric Constraints

  16. Conclusions • Component–based person detection • Better than full-body person detector • Hierarchical Classifiers or Adaptive Combination of Classifiers (ACC)

  17. Future Work • Face detection: Heisele et al. CVPR’01 • Face recognition: Heisele et al. CVIU’03 • Car detection: Bileschi, Leung and Rifkin ECCV 04 Workshop • Arbitrary viewpoints? • How appearance and geometric configuration change

  18. Questions • Lighting • Videos • Space-time component based detection, recognition and tracking • Other applications • Insect • What are meaningful components? • Object detection/recognition/tracking if cameras intrinsic and extrinsic parameters may change

  19. Thank You!

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