1 / 15

Face Tracking and Person Action Recognition - Update

Face Tracking and Person Action Recognition - Update. Sascha Schreiber. Overview. Recapitulation of methodology for action recognition Face tracking with I-Condensation Recognition performance comparison on actions from the m4 dataset Kalman filtering of occluded gestures Outlook.

pandora-farmer
Télécharger la présentation

Face Tracking and Person Action Recognition - Update

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. Face Tracking and Person Action Recognition - Update Sascha Schreiber

  2. Overview • Recapitulation of methodology for action recognition • Face tracking with I-Condensation • Recognition performance comparison on actions from the m4 dataset • Kalman filtering of occluded gestures • Outlook

  3. Person Action Recognition Extraction of person locations Face detection/Blob tracking Feature calculation Global Motion Features Temporal segmentation Bayesian Information Criterion Classification of segments Hidden Markov Models Actions, timestamps

  4. Person Action Recognition Extraction of person locations Face detection/Blob tracking Feature calculation Global Motion Features Temporal segmentation Bayesian Information Criterion Classification of segments Hidden Markov Models Actions, timestamps

  5. Face Tracking • Sampling from importance function for reinitialisation • Importance sampling with weighting correction factor • Standard Condensation sampling • Nweighted particles • Updating using their likelihood Automatic initialization by pyramid sampling and MLP classification Particle filtering with ICondensation • Sampling from prediction density  Introduction of importance function: skin color distribution

  6. Performance of Face Tracking Demonstration of difference between: Standard Condensation ICondensation

  7. Person Action Recognition Extraction of person locations Face detection/Blob tracking Feature calculation Global Motion Features Temporal segmentation Bayesian Information Criterion Classification of segments Hidden Markov Models Actions, timestamps

  8. Recognition Performance m4 IDIAP training data (TRN 01-30), IDIAP test data (TST 01-30) Continuous HMMs (6 states, 3 mixtures)

  9. Recognition Performance m4 IDIAP training data (TRN 01-30), IDIAP test data (TST 01-30) Discrete HMMs (6 states, codebook 1500)

  10. Occluded Gestures Scenario: Person walking on front of a tracked object Video-stream Featurestream Feature- extraction Compensation of occlusion Occlusion Smoothed featurestream Segmented Featurestream Classification result Classification Stream- segmentation

  11. Occluded Gestures Time update equation Measurement update equation Application for Kalman filtering: • Discrete-time process: • Calculation of an estimate

  12. Occluded Gestures Kalman- filter • One general Kalman-Filter for the disturbed featurestream • N action-specialized Kalman-Filters, each trained for a special gesture to be recognized by the HMM Kalman- filter Kalman- filter Kalman- filter Improving featurestream by smoothing with :

  13. Performance of Kalman filtering IDIAP training data (TRN 01-30), IDAP test data (TST 01-30) Continuous HMMs (6 states, 3 mixtures)

  14. Outlook • Implementation of extended Kalman filter • Head orientation tracking • Integration of face recognition into particle filter • Further improvement of action detection on m4 data • Connection to Meeting Segmentation / Multimodal Recognizer

  15. Face Tracking and Person Action Recognition - Update Sascha Schreiber

More Related