The document proposes a perceptual mixture-of-Gaussians approach to background subtraction that adapts dynamically based on principles of human visual perception. It models the relationship between detectable foreground pixels and background pixels based on Weber's law of just-noticeable differences. This results in piecewise linear relationships and different adaptation speeds for scotopic and photopic vision. An evaluation of 50 test sequences shows the proposed approach achieves more realistic background value prediction, higher stability across scenarios, and superior detection quality compared to other statistical methods.
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
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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
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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
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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
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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
bx
20 log 255
10 b x
1
2TP
b
Te
Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed
December 30, 2013
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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
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13. Error Sensitivity in Darker Background
Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed
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14. Piece-wise Liner Relationship
Scotopic Vision (R)
Photopic Vision (C)
Te
Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed
December 30, 2013
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15. Dynamic Adaptation Speed
•Sleeping person problem
•Walking person problem
Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed
December 30, 2013
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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
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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
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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
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