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Head Tracking and Action Recognition in a Smart Meeting Room

Head Tracking and Action Recognition in a Smart Meeting Room

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Head Tracking and Action Recognition in a Smart Meeting Room

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  1. Head Tracking and Action Recognition in a Smart Meeting Room Hammadi Nait-Charif & Stephen J. McKenna

  2. Objectives • Simultaneously track multiple people • Perform automatic initialization • Handle Person-Person Occlusion • Combine data from the 2 cameras to annotate the activity of all 6 participants

  3. SIS Algorithm

  4. SIS Algorithm Solutions to degeneracy problem • Good Choice of Important Density sub-optimal solution => 2. Resampling

  5. SIR Algorithm

  6. Drawbacks of SIR Algorithm • Particles with high weight a statistically selected many times which leads to the loss of diversity of the particles • Prior is not the optimal choice for the importance function because it does not take into account the latest observation 3. SIR tends to be less accurate with smaller sample sets

  7. ILW Algorithm

  8. How ILW is Better • Half of the particles migrate to the high likelihood regions (because they take into account the latest measurement) in the state-space while the other half are sampled from the prior • So even if the prior is poor the estimate is better due the use of the other half of the particles in the high likelihood regions

  9. Figure 1

  10. Figure 2

  11. Figure 3

  12. Figure 4

  13. Figure 5

  14. Figure 6

  15. Figure 7

  16. Figure 8