Background Subtraction Using Ensembles of Classifiers with an ...

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PETS 2001 - Total Performance10.90.8True Positives0.70.60.5RGBRGB and GradientAll FeaturesGradientHaar0 0.002 0.004 0.006 0.008 0.01 0.012False PositivesFigure 5.10. Overall results on the PETS 2001 datasetPETS 2001 - Total Performance for Individual Features10.90.8True Positives0.70.60.50.4GrayRGBGradient MagnitudeGradient OrientationHaar 1Haar 2Haar 3Haar 4Haar 5Haar 6Haar 7Haar 80 0.01 0.02 0.03 0.04 0.05False PositivesFigure 5.11. Results for each individual feature on the PETS 2001 dataset42
0.06All FeaturesRGBFrame-by-Frame Performance - PETS 20010.050.04False Positives0.030.020.0100 5 10 15 20Figure 5.12. Frame by frame false positive results on PETS 2001 datasetperformance of the RGB feature set was quite competitive with the extended feature set. It is stillthe case, though, that the extended feature set provided the optimal performance with respectto the other feature set combinations. The poor performance when using only the Haar featuresand only the gradient features are quite notable as well. Despite the disappointing performanceof these features they still manage to augment the RGB features and make the extended featureset the optimal classifier.In Figure 5.16, the results of the individual features classification on the PETS 2006 data setare shown. The RGB clearly outperforms all other features, including the gradient magnitude.This finalizes the trend seen now in all three data sets that the individual features do not offerany consistent improvement over the RGB feature set, while using all of these features in anensemble classifier outperforms the RGB features every time.Figure 5.17 contains the performance at individual frames for the PETS 2006 data set. Theextended feature set appears less consistent in the data set.This may be rather misleading,however, because the false alarm rate is so low that the inconsistencies become exaggerated.The PETS 2006 data set offered the lowest performance gains when comparing the extendedfeature set the RGB features. The primary reason for this is that this data set is not a terribly43
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 46 and 47: 1OTCBVS - Total Performance0.90.80.
Page 48 and 49: (a) Original Image (b) All Features
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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