Background Subtraction Using Ensembles of Classifiers with an ...

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1OTCBVS - Total Performance0.90.80.7True Positives0.60.50.40.30.20.1RGBRGB and GradientAll FeaturesGradientHaar0 0.05 0.1 0.15 0.2False PostivesFigure 5.5. Overall results on the OTCBVS dataset1OTCBVS - Total Performance for Individual Features0.90.80.7True Positives0.60.50.40.30.2GrayRGBGradient MagnitudeGradient OrientationHaar 1Haar 2Haar 3Haar 4Haar 5Haar 6Haar 7Haar 80.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4False PostivesFigure 5.6. Results for each individual feature on the OTCBVS dataset38
0.25All FeaturesRGBFrame-by-Frame Performance - OTCBVS0.2False Positives0.150.10.0500 2 4 6 8 10 12 14 16Figure 5.7. Frame by frame false positive results on OTCBVS dataset(a) Original Image(b) Threshold=220(c) Threshold=230(d) Threshold=240Figure 5.8. RGB classification image of an OTCBVS frame39
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 and 32: ox (V r ) is: V r = IN(26, 26) + IN
Page 34 and 35: Figure 4.1. High level few of ensem
Page 36 and 37: variance are able to adapt, an init
Page 38 and 39: CHAPTER 5RESULTS5.1 MethodologyIn o
Page 40 and 41: Table 5.1. Description of data sets
Page 42 and 43: Table 5.2. Frames used for training
Page 44 and 45: The ground truth for the test sets
Page 48 and 49: (a) Original Image (b) All Features
Page 50 and 51: PETS 2001 - Total Performance10.90.
Page 52 and 53: (a) Original Image(b) Threshold=220
Page 54 and 55: PETS 2006 - Total Performance10.950
Page 56 and 57: features false positives and a high
Page 58 and 59: (a) PETS 2006 Original Image(b) PET
Page 60 and 61: CHAPTER 6CONCLUSION6.1 SummaryTradi
Page 62 and 63: OTCBVS - Summary10.90.8True Positiv
Page 64 and 65: also different than using an unanim
Page 66 and 67: [15] Z. Hou and C. Han. A backgroun
Page 68: [52] OTCBVS Benchmark Dataset Colle

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