1 / 22

Gesture Recognition Using 3D Appearance and Motion Features

Guangqi Ye, Jason J. Corso, Gregory D. Hager Computational Interaction and Robotics Lab The Johns Hopkins University Baltimore, MD. Gesture Recognition Using 3D Appearance and Motion Features. Analogy Between Gesture and Speech. 4DT Platform.

sengland
Download Presentation

Gesture Recognition Using 3D Appearance and Motion Features

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Guangqi Ye, Jason J. Corso, Gregory D. Hager Computational Interaction and Robotics Lab The Johns Hopkins University Baltimore, MD Gesture Recognition Using 3D Appearance and Motion Features

  2. Analogy Between Gesture and Speech

  3. 4DT Platform • Previous work: J. Corso, D. Burschka, G. Hager, The 4DT: Unencumbered HCI With VICs. CVPRHCI, 2003. • Geometrically and photometrically calibrated • Known background

  4. Video Preprocessing Acquisition Rectification Color Calibration Background Subtraction

  5. System Framework Image Preprocessing Coarse Stereo Matching Appearance/Motion Extraction Feature Clustering Gesture Recognition

  6. Visual Feature Capturing: 3D Volume • Consider limited 3D space around object • Block-based coarse stereo matching

  7. Motion Computation • Motion by differencing of stereo volume

  8. Unsupervised Learning of Feature Set • VQ: K-means approach • Choice of cluster number based on distortion analysis

  9. Temporal Gesture Modeling • 6-state discrete forward HMMs • Multilayer Neural Network Aligning all sequences to have equal length 3-layers, 50 hidden nodes

  10. Experiment: Gesture Vocabulary Push Toggle

  11. Gesture Vocabulary Swipe Left Swipe Right

  12. Gesture Vocabulary Twist Clockwise Twist Anti-clockwise

  13. Different Feature Data Sets • Appearance volume • 5x5x5=125 • 10x10x10=1000 • Motion volume • Concatenation of appearance and motione.g.,(125-appearance, 1000-d motion) • Combination of clustering result of appearance and motion • Form a 2-d vector of cluster identity e.g., (3, 2)

  14. Gesture Recognition • Training: >100 sequences for each gesture • Test: >70 sequences for each gesture • Combination achieves best results

  15. Real-time Implementation Demo

  16. Conclusion • Novel approach to extract 3D appearance and motion cues without tracking • VQ clustering to learn gesteme • Modeling dynamic gestures using HMM, NN • Real-time implementation on 4DT • Extensive experiments achieve high recognition accuracy

  17. Thanks

  18. 3D Appearance Volume • Comprehensive color normalization • Coarse disparity map Consider local images of m x n patches, perform pair-wise image matching between patches • Disparity search range [0, ( p-1 ) * w ] • Dimensionality 3D volume m*n*p

  19. Gesture Recognition • HMM modeling on collapsed sequencesRaw: 6 6 6 5 5 5 5 5 4 4 4 4 1 1Collapsed: 6 5 4 1 • Without considering duration

  20. 4DT Platform • Gestures in visual HCI: popular choice • Manipulative gesture modeling without tracking Difficulty of reliable tracking of hand Complexity of hand modeling • 3D data acquisitionLimitation of 2D cues for modeling handStereo matching Special sensors

  21. Properties of 4DT • Known background & object properties

More Related