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3D Motion Data Mining

3D Motion Data Mining. Multimedia Project Multimedia and Network Lab, Department of Computer Science. Introduction. 3D Motion Capture. Integration. Gait Analysis 3D Motion Capture Motion correlation and error modeling Humanoid robotics Game Control Disease diagnostic

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3D Motion Data Mining

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  1. 3D Motion Data Mining Multimedia Project Multimedia and Network Lab, Department of Computer Science

  2. Introduction • 3D Motion Capture UTD Multimedia and Networking Lab

  3. Integration • Gait Analysis • 3D Motion Capture • Motion correlation and error modeling • Humanoid robotics • Game Control • Disease diagnostic • Motion Clacification UTD Multimedia and Networking Lab

  4. Gait? • Study of Human Walk (Lower Limbs) • Terminology • Gait cycle: Begins when one foot contacts the ground and ends when that foot contacts the ground again UTD Multimedia and Networking Lab

  5. 3D Input Data (MoCap & EMG) EMG data 3D Mocap data M x 54 Matrix ( M is the total num of Frames ) UTD Multimedia and Networking Lab

  6. Input Data Format • Each Motion is represented by set of joint vectors • Use sliding Windows for feature extractions Tibia Foot Toe Windows (Time Frame) UTD Multimedia and Networking Lab

  7. Motion Data Analysis Flow Data Collection Preprocessing Feature Extraction Data Analysis Geometric Trans. Motion capture Cross-Pair 24 Feature Point Gait Cycle

  8. UTD Multimedia and Networking Lab

  9. Project I: Content-Based Indexing • Input Data MoCap EMG WSVD WSVD WSVD IAV + FCM where, c - pre-determined number of clusters M – Total number of overall windows center - center/median points for all c clusters in k-d space U - “degree of membership” for each M points with respect to each cluster. Reduced dimensional vector

  10. Hierarchical Spatial Grid(HSG) UTD Multimedia and Networking Lab

  11. HSG (2D) 1 1 1 2 2 2 3 3

  12. Even distribution transformation • Transforming the points into corresponding rank index on each dimension • rank index (A(i))= rwhere, array A with non-zero values, non-increasing ordered array A_RI, A_RI(r)=A(i)

  13. Similarity search • Given a query Q(using IAV, WSVD, and FCM) • Q  [qj,min, qj,max] • Where • Distance

  14. Project Goal • Goal: Building Disk base Input/output functions for Hierarchical Spatial Grid Indexing Structure • Input: Spatial data(2D,3D,…) • Input query : Spatial data(2D,3D,…) • Output: related Spatial data(2D,3D,…) • Requirement: • Disk based input/output Indexing Structure • Language option: C or JAVA (prefer C) • Reference : G. Pradhan and B. Prabhakaran, " Indexing 3D Human Motion Repositories For Content-based Retrieval," IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE , VOL. 13, NO. 5, , SEPTEMBER 2009 • http://multimedia.utdallas.edu/pub.jsp UTD Multimedia and Networking Lab

  15. Project II: Pyramid Matching Kernel • Place multi-dimensional, multi-resolution grid over point sets • Consider points matched at finest resolution where they fall into same grid cell • Approximate similarity between matched points with worst case similarity at given level Pyramid match kernel measures similarity of a partial matching between two sets: No explicit search for matches!

  16. Project II: Pyramid Matching Kernel • Pyramid Match Kernel? • measures similarity of a partial matching between two sets • Place multi-dimensional, multi-resolution grid over point sets • Consider points matched at finest resolution where they fall into same grid cell • Approximate similarity between matched points with worst case similarity at given level UTD Multimedia and Networking Lab

  17. Pyramid match • http://www.cs.utexas.edu/~grauman/research/projects/pmk/pmk_projectpage.htm UTD Multimedia and Networking Lab

  18. Number of newly matched pairs at level i Measure of difficulty of a match at level i PMK :Approximate partial match similarity UTD Multimedia and Networking Lab

  19. histogram pyramids number of newly matched pairs at level i measure of difficulty of a match at level i PMK • Weights inversely proportional to bin size • Normalize kernel values to avoid favoring large sets

  20. Project II (Cont.) • Goal: Motion Classification using Pyramid Matching Kernel (PMK) for fast matching motion features. • Feature Extraction • 3D Geometric feature extractions(Detail points are provided Later) • Classification: HMM or GMM • Programming Language: C, JAVA, or Matlab • Reference : • K. Grauman and T. Darrell. “The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features,”In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005. • C++ code: http://people.csail.mit.edu/jjl/libpmk/ UTD Multimedia and Networking Lab

  21. Project III • Goal: Motion animation Tool with selected frames. e.g., UTD Multimedia and Networking Lab

  22. Question? Thank You ! UTD Multimedia and Networking Lab

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