Background Subtraction Using Ensembles of Classifiers with an ...

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LIST OF FIGURESFigure 1.1 Surveillance tracking system flowchart 1Figure 1.2 Background classification using only RGB features 3Figure 1.3 Background classification using ensemble classifier 5Figure 3.1 Set of Haar features being used 22Figure 3.2 Illustration of equation to compute Haar value 22Figure 3.3 Effects of varying illumination on different features 24Figure 4.1 High level few of ensemble algorithm 26Figure 5.1 Sample frames from OTCBVS data set 32Figure 5.2 Sample frames from PETS 2001 data set 33Figure 5.3 Sample frames from PETS 2006 data set 33Figure 5.4 Example of thresholding a classifier hypothesis 35Figure 5.5 Overall results on the OTCBVS dataset 38Figure 5.6 Results for each individual feature on the OTCBVS dataset 38Figure 5.7 Frame by frame false positive results on OTCBVS dataset 39Figure 5.8 RGB classification image of an OTCBVS frame 39Figure 5.9 Ensemble classification image of an OTCBVS frame 40Figure 5.10 Overall results on the PETS 2001 dataset 42Figure 5.11 Results for each individual feature on the PETS 2001 dataset 42Figure 5.12 Frame by frame false positive results on PETS 2001 dataset 43Figure 5.13 RGB classification image of a PETS 2001 frame 44Figure 5.14 Ensemble classification image of a PETS 2001 frame 45Figure 5.15 Overall results on the PETS 2006 dataset 46Figure 5.16 Results for each individual feature on the PETS 2006 dataset 47iii
Figure 5.17 Frame by frame false positive results on PETS 2006 dataset 47Figure 5.18 Weak hypotheses fused into a single strong hypothesis 49Figure 5.19 Effect of object size on gradient magnitude 50Figure 5.20 Classification using ensemble and gradient magnitude 51Figure 6.1 False positive results at 90% true positive rate 53Figure 6.2 OTCBVS ROC graph summary 53Figure 6.3 PETS 2001 ROC graph summary 54iv
Page 1 and 2: Background Subtraction Using Ensemb
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 46 and 47: 1OTCBVS - Total Performance0.90.80.
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
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PETS 2006 - Total Performance10.950
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features false positives and a high
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(a) PETS 2006 Original Image(b) PET
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CHAPTER 6CONCLUSION6.1 SummaryTradi
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OTCBVS - Summary10.90.8True Positiv
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also different than using an unanim
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[15] Z. Hou and C. Han. A backgroun
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[52] OTCBVS Benchmark Dataset Colle
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