Background Subtraction Using Ensembles of Classifiers with an ...

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(a) Original Image, no shadow(b) Original Image, shadow(c) Hue Image, no shadow(d) Hue Image, shadow(e) Luma from YCbCr, no shadow(f) Luma from YCbCr, shadow(g) Gradient Magnitude, no shadow(h) Gradient Magnitude, shadowFigure 3.3. Effects <strong>of</strong> varying illumination on different features24
Page 1 and 2: Background Subtraction Using Ensemb
Page 6 and 7: LIST OF FIGURESFigure 1.1 Surveilla
Page 8: Background Subtraction Using Ensemb
Page 11 and 12: (a) Simple Frame(b) Simple Frame Cl
Page 13 and 14: (a) Simple Frame(b) Simple Frame Cl
Page 15 and 16: CHAPTER 2RELATED WORKS2.1 Early Wor
Page 17 and 18: K∑P (X t ) = w t (k) · η(X t ,
Page 19 and 20: ŝ t (p) = ŝ t−1 + K(p, t) · (I
Page 22 and 23: Segmenting images through the conce
Page 24 and 25: present the color space in Equation
Page 28: CHAPTER 3FEATURESStandard bitmaps s
Page 31: ox (V r ) is: V r = IN(26, 26) + IN
Page 35 and 36: of distributions observed exceeds K
Page 37 and 38: In both [40] and [41], the average
Page 39 and 40: from Classifier A will certainly no
Page 41 and 42: Figure 5.2. Sample frames from PETS
Page 43 and 44: (a) Original Image(b) Non-Threshold
Page 45 and 46: In Figure 5.6, an ROC graph of the
Page 47 and 48: 0.25All FeaturesRGBFrame-by-Frame P
Page 49 and 50: 2001 data set. The same dramatic im
Page 51 and 52: 0.06All FeaturesRGBFrame-by-Frame P
Page 53 and 54: (a) Original Image(b) All Features,
Page 55 and 56: PETS 2006 - Total Performance for I
Page 57 and 58: (a) Original Image(b) Haar 1 Classi
Page 59 and 60: 1OTCBVS - Total Performance0.90.80.
Page 61 and 62: Figure 6.1. False positive results
Page 63 and 64: efficient classifier when being res
Page 65 and 66: REFERENCES[1] R. Jain, R. Kasturi,
Page 67 and 68: [32] O. Javed, K. Shafique, and M.

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