Background Subtraction Using Ensembles of Classifiers with an ...

More documents

Recommendations

Info

PETS 2006 - Total Performance10.950.90.85True Positives0.80.750.70.650.6RGBRGB and GradientAll FeaturesGradientHaar0.005 0.01 0.015 0.02 0.025False PostivesFigure 5.15. Overall results on the PETS 2006 datasetdifficult data set. Because it is an indoor environment and the foreground objects are in closeproximity to the camera there is little room for improvement from the standard RGB features.Despite this a slight improvement was still seen when incorporating gradient features and Haarfeatures. In real world use it would be unlikely to use these additional features, however, due tothe increased computational load they incur and the marginal gains they offer.5.4.4 Feature PerformanceThe performance of classifiers only using a single feature from the extended feature set waspoor, as observed in Figures 5.6, 5.11, and 5.16. When these weak classifiers are combined intoan ensemble of classifiers, the meta-classification offers substantial results. This adheres to thetheory of ensemble learning presented by Schapire in [34].An example of how a series of weak classifier hypotheses are able to result in a strong hypothesisin background classification is shown in Figure 5.18, where the 8 hypotheses from eachindividual Haar feature are shown as well as the fusion of those classifiers. The Haar classifierclearly has a tendency to over classify pixels are foreground, resulting in many false positives.This is evidenced in the ROC graphs where the classifiers using individual Haar features will bothmaximize the true positives and false positives. Fortunately there is a low overlap between these46
PETS 2006 - Total Performance for Individual Features10.90.80.7True Positives0.60.5GrayRGB0.4Gradient MagnitudeGradient OrientationHaar 10.3Haar 2Haar 3Haar 40.2Haar 5Haar 6Haar 7Haar 80.10 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045False PostivesFigure 5.16. Results for each individual feature on the PETS 2006 dataset0.012Frame-by-Frame Performance - PETS 2006All FeaturesRGB0.010.008False Positives0.0060.0040.00200 2 4 6 8 10 12 14 16 18 20Figure 5.17. Frame by frame false positive results on PETS 2006 dataset47
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 50 and 51: PETS 2001 - Total Performance10.90.
Page 52 and 53: (a) Original Image(b) Threshold=220
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
show all

Background Subtraction Using Ensembles of Classifiers with an ...

Create successful ePaper yourself

Delete template?

Save as template?