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A Hierarchical Model dedicated to Motion Analysis

A Hierarchical Model dedicated to Motion Analysis. Thomas Fourès - Philippe Joly. Objectives. Generic model for a given application Different levels of description Adaptation to : Current resolution User’s Needs Application to the human body. Application Context. ?.

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A Hierarchical Model dedicated to Motion Analysis

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  1. A Hierarchical Model dedicated to Motion Analysis Thomas Fourès - Philippe Joly

  2. Objectives • Generic model for a given application • Different levels of description • Adaptation to : • Current resolution • User’s Needs • Application to the human body

  3. Application Context ? Frame from a sequence Posture description Human body model

  4. Hierarchical modeling • Different description levels : Coarse representation -> sharp representation • One level is based on result with previous one • Description refined at each iteration Model level : 0 1 2 3 Representation : coarse detailed

  5. Example Original frame Level 0 : BD Level 3 Level 1 Level 2

  6. Model Matching • Model elements defined as regions • Matching one element  find its most probable location in an image area • Take into account physical constraints (articulated model) • Definition of search areas

  7. Model Matching • Algorithm to match model parts (at any level) : Definition of search areas Distance Map Element Matching Previous result Physical Constraints

  8. Results

  9. Results

  10. Results

  11. Further Works • Deal with many people in camera field • Motion description

  12. People Recognition by Costume Descriptors Gaël Jaffré - Philippe Joly

  13. Framework • Use costume in video content indexing : • role of a character • clothes specific to a profession • classes of characters • information about the document • date, weather • Application : • people recognition

  14. Costume Feature Extraction (1) (2) • (1) Face detection • (2) Approximation of costume localization • (3) Feature extraction (3)

  15. Costume Features & Decision Texture Dominant Color Color Histograms • Earth • Mover’s distance (EMD) Bhattacharyya coefficient Euclidian distance

  16. Improvements Face detector false detections :

  17. Improvements • False detections : • Temporal approach : keep candidate faces that appear at least N2 times out of N1 frames • Non detections : • Shot generalization notion : characters present in a frame are also in each frame of the shot

  18. Improvements Shot 2 Shot 1

  19. Application : people recognition • Goal : • Automatic detection of each character occurrence in a video sequence • Automatic labeling

  20. Application : people recognition Person detection Costume localization Feature extraction Is the feature in our database? Yes No Character recognized Add the costume in the DB

  21. Results

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