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HUMAN MOTION VIDEO DATABASE

HUMAN MOTION VIDEO DATABASE. Scripts, Queries, Recognition. Jezekiel Ben-Arie ECE Department University Of Illinois at Chicago. Composition of interactive motion queries. Analysis and Recognition of human activities. Human body parts labeling. Human detection.

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HUMAN MOTION VIDEO DATABASE

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  1. HUMAN MOTION VIDEO DATABASE Scripts, Queries, Recognition Jezekiel Ben-Arie ECE Department University Of Illinois at Chicago

  2. Composition of interactive motion queries. • Analysis and Recognition of human activities. • Human body parts labeling. • Human detection.

  3. HUMAN ACTIVITY CAPTURE AND REGONITION

  4. Visual Feedback User Motion Query Video Retrieval Retrieved videos Video Database Videos Video Analysis and Recognition

  5. HUMAN BODY PART LABELING • Objective: Identify the roles of parts that appear as bars. • Labeling : Using the spatial locations and orientations. • Method : Finding maximum conjunction of partial hypotheses.

  6. HUMAN BODY PART LABELING Theoretical Foundations

  7. HUMAN BODY PART LABELING Illustration of Theoretical Foundations (b) (a) Overlap of Spatial distribution for (a) Correct Labeling (b) Incorrect Labeling

  8. HUMAN BODY PART LABELING (b) (a) Mesh diagram of Overlap of Spatial distribution for (a) Correct Labeling (b) Incorrect Labeling

  9. HUMAN BODY PART LABELING Experimental Results • Silhouette Extraction • Bar detection Using Gabor signatures. Parsing silhouettes • 90 different human poses • 98.7% correct labeling.

  10. HUMAN BODY PART LABELING Experimental Results

  11. HUMAN BODY PART LABELING Experimental Results

  12. HUMAN BODY PART LABELING Silhouette Extraction

  13. HUMAN BODY PART LABELING Silhouette Extraction Illustration of variation of chromaticity and brightness distortion

  14. HUMAN ACTIVITY RECOGNITION Introduction • Poses indicative of actions taking place Poses involved in walking • Indexing based recognition using sparse frames • Extends this technique with optimal constrained sequencing based voting

  15. HUMAN ACTIVITY RECOGNITION Introduction • Temporal sequence of pose vectors • Multidimensional hash tables for model activities • Individual hash tables for each body part • Identifying input pose vectors as samples of densely sampled model activity and create vote vectors • Vote vectors are temporal depiction of the log-likelihood that indexed pose belongs to a model • Dynamic programming based constrained sequencing to recognize activities

  16. HUMAN ACTIVITY RECOGNITION Creating Vote Vectors Illustration of the entire voting process

  17. HUMAN ACTIVITY RECOGNITION Experimental Results Videos of sitting action overlaid with skeleton superposed with the help of tracking information Sparse samples of jump activity adequate for robust recognition

  18. HUMAN ACTIVITY RECOGNITION Experimental Results Average votes for 5 test videos of each activity along with the votes for other activities. Rows – Test Activity Columns – Model Activity Recognition rate under various conditions of occlusion

  19. HUMAN ACTIVITY RECOGNITION Experimental Results Performance of the approach under conditions of view point variance

  20. FACE DETECTION Original Image Skin detection Regions passing the ellipse area criterion Detection by the Gabors Detected Faces

  21. FACE DETECTION Detected faces with medium threshold (0.7) Detected faces with maximum threshold (0.8) Original Image

  22. GUI for Queries Composition • Motion query is composed by using model motion data clips. • An example of a model motion data clip is a walk cycle consisting of a sequence of poses in one basic cycle of left-right steps. • Model motion data clip can also be non-cyclic such as sitting. • Model motion data clip is obtained from a motion capture library or can be interactively composed by the user.

  23. INTERACTIVE GUI Specify Trajectory Key-points Interpolate by Splines Specify Activities Calculate Segments Calculate Position and Orientations Generate Motion Sequences(Scripts) Display

  24. Theoretical Foundations • Parameterization of 3-D rotations (Euler Quaternions) • Splines (Catmull Rom) • Interpolation (SLERP, Quaternions) • Human body model • Motion composition techniques (Inverse Kinematics, Mocap)

  25. Limb Pose Vocabulary

  26. Example of complete body poses

  27. Inverse kinematics based key framing tool

  28. Implementation

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