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A Hierarchical Model dedicated to Motion Analysis

This work presents a hierarchical model for motion analysis focusing on the human body. It features various levels of representation from coarse to detailed descriptions and adapts to user needs and current resolution. The model incorporates an algorithm for matching model elements and accounts for physical constraints. It applies costume feature extraction for character recognition in video content, improving face detection and temporal consistency. Ultimately, the aim is to facilitate automatic detection and labeling of characters based on costume descriptors in video sequences.

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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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