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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.

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Face Tracking and Person Action Recognition - Update

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  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

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