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Talk 2012-icmew-perception

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  • 1. Information Technology Robust Background Subtraction Based on Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed Mahfuzul Haque and Manzur Murshed
  • 2. Agenda  Background Subtraction  Statistical Background Subtraction  Perception Inspired Background Subtraction  Dynamic Adaptation Speed  Experiments  Summary  Q&A Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 2
  • 3. Background Subtraction Input Output Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 3
  • 4. Background Subtraction: Challenges Basic Background Subtraction (e.g., BBS) - Current frame Challenges = Background Foreground Blob Dynamic Background Subtraction(e.g., MOG) Background     Illumination variation Local background motion Camera displacement Shadow and reflection Model Current frame Foreground Blob Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 4
  • 5. Statistical Background Subtraction ω1 ω2 ω3 σ12 σ22 σ32 µ1 µ2 µ3 road car shadow 65% 20% Statistical Approaches x x P(x) Our Hypothesis (Perception Inspired) x BBS: x = c MOG: x = c1σ μ 15% x x = c2b P(x) Te b Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 5
  • 6. Perception Inspired Background Subtraction x = c2 b Current Frame Detection with Low x Detection with High x x x P(x) b Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 6
  • 7. Weber’s Law How human visual system perceives noticeable intensity deviation from the background? Ernst Weber, an experimental psychologist in the 19th century, observed that the just-noticeable increment ΔI is linearly proportional to the background intensity I. ΔI = c2I Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 7
  • 8. Weber’s Law Ernst Weber, an experimental psychologist in the 19th century, observed that the just-noticeable increment ΔI is linearly proportional to the background intensity I. ? x x ΔI = c2I x x = c2 b P(x) b Te b Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 8
  • 9. Perceptual tolerance of HVS What is the perceptual tolerance level in distinguishing distorted intensity measures? p dB Method 1 Reference q dB Method 2 Image Distorted Images |p – q| < 0.5 dB Not perceivable by human visual system Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 9
  • 10. Our Problem: c2 = ? x = c2 b x x P(x) Weber’s Law x = c2b Perceptual Threshold, TP (0.5 dB)  255 20 log10   bx      20 log  255 10  b  x      1  2TP  b Te Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 10
  • 11. Linear Relationship x b Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 11
  • 12. Rod and Cone  Rods and Cones are two different types of photoreceptor cells in the retina of human eye  Rods – Operate in less intense light – Responsible for scotopic vision (night vision)  Cones – Operate in relatively bright light – Responsible for photopic (color vision) Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 12
  • 13. Error Sensitivity in Darker Background Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 13
  • 14. Piece-wise Liner Relationship Scotopic Vision (R) Photopic Vision (C) Te Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 14
  • 15. Dynamic Adaptation Speed •Sleeping person problem •Walking person problem Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 15
  • 16. Dynamic Adaptation Speed Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 16
  • 17. Dynamic Adaptation Speed Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 17
  • 18. Dynamic Adaptation Speed Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 18
  • 19. Experiments Test Sequences  Total 50 test sequences from 8 different sources  Scenario distribution      Indoor Outdoor Multimodal Shadow and Reflection Low background-foreground contrast False Classification Evaluation  Qualitative and quantitative comparison:  MOG (S&G) (TPAMI, 2000) False Positive (FP) False Negative (FN)  MOG (Lee) (TPAMI, 2005)  ViBe (TIP, 2011) Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 19
  • 20. Test Sequences PETS (9) Wallflower (7) UCF (7) IBM (11) CAVIAR (7) Te VSSN06 (7) Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed Other (2) December 30, 2013 20
  • 21. Experiments Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 21
  • 22. Experiments Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 22
  • 23. Experiments Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 23
  • 24. First Frame Test Frame Ground Truth MOG (S&G) MOG (Lee) ViBe Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed Proposed December 30, 2013 24
  • 25. Summary  Realistic background value prediction: high model agility and superior detection quality at fast learning rate.  No context related information: high stability across changing scenarios.  Perception based detection threshold: superior detection quality in terms of shadow, noise, and reflection.  Perceptual model similarity: optimal number of models throughout the system life cycle.  Parameter-less background subtraction: ideal for realtime video analytics. Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 25
  • 26. Q&A Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed December 30, 2013 26

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