1. MVL (Machine Vision Lab) UIC HUMAN MOTION VIDEO DATABASE Jezekiel Ben-Arie ECE Department University Of Illinois at Chicago Scripts, Queries, Recognition

2. MVL (Machine Vision Lab) UIC  Composition of interactive motion queries.  Analysis and Recognition of human activities.  Human body parts labeling.  Human detection.

3. MVL (Machine Vision Lab) UIC HUMAN ACTIVITY CAPTURE AND REGONITION

4. MVL (Machine Vision Lab) UIC Motion Query Video Retrieval Retrieved videos Video Database Videos Video Analysis and Recognition User Visual Feedback

5. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC Theoretical Foundations HUMAN BODY PART LABELING

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

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

9. MVL (Machine Vision Lab) UIC HUMAN BODY PART LABELING Experimental Results / Silhouette Extraction / Bar detection Using Gabor signatures. Parsing silhouettes / 90 different human poses / 98.7% correct labeling.

10. MVL (Machine Vision Lab) UIC HUMAN BODY PART LABELING Experimental Results

11. MVL (Machine Vision Lab) UIC HUMAN BODY PART LABELING Experimental Results

12. MVL (Machine Vision Lab) UIC HUMAN BODY PART LABELING Silhouette Extraction

13. MVL (Machine Vision Lab) UIC HUMAN BODY PART LABELING Silhouette Extraction Illustration of variation of chromaticity and brightness distortion

14. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC HUMAN ACTIVITY RECOGNITION Creating Vote Vectors Illustration of the entire voting process

17. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC HUMAN ACTIVITY RECOGNITION Experimental Results Performance of the approach under conditions of view point variance

20. MVL (Machine Vision Lab) UIC FACE DETECTION Original ImageSkin detectionRegions passing the ellipse area criterion Detection by the GaborsDetected Faces

21. MVL (Machine Vision Lab) UIC FACE DETECTION Original Image Detected faces with medium threshold (0.7) Detected faces with maximum threshold (0.8)

22. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC Specify Trajectory Key-points Interpolate by Splines Specify Activities Calculate Segments Calculate Position and Orientations Generate Motion Sequences(Scripts) Display INTERACTIVE GUI

24. MVL (Machine Vision Lab) UIC 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. MVL (Machine Vision Lab) UIC Limb Pose Vocabulary

26. MVL (Machine Vision Lab) UIC Example of complete body poses

27. MVL (Machine Vision Lab) UIC Inverse kinematics based key framing tool

28. MVL (Machine Vision Lab) UIC Implementation