Background Subtraction Using Ensembles of Classifiers with an ...

More documents

Recommendations

Info

Segmenting images through the concept of image layers is a popular approach to binarysegmentation, where the layers represent different planes or object groups in an image Typicallymany layers will represent both the foreground and background. A foreground layer is generallyconsidered a layer that has spatiotemporal change.Criminisi et al. perform background classification using image layering in [22]. A probabilisticapproach is used where the motion, color, and contrast are combined with the spatial and temporalpriors. This use of motion cues is generally a limiting feature due to the computational demands,however the algorithm presented is able to run in real-time because the actual pixel velocities arenever computed. Instead a binary motion value is assigned to represent the presence or absenceof motion.In [23], Patwardhan et al. present an automated method of pixel layering. The algorithm has along initialization step followed by a simpler detection process. T frames are used in initialization.The first frame is segmented using the following procedure. The maximum of the histogram ofthe gray level pixel intensities, h max is found, and all pixels whose gray levels are within h max ± ρare added to the initial layer, where ρ is derived from the image covariance matrix. A samplingof the about 10-20% of the image is used to perform a Kernel Density Estimation (KDE), whichgenerates a probability density function for the initial layer based on a pixel’s feature vector (inthis case RGB features are mapped to the space presented in [24], which is discussed further inSection 2.3.). Based on the KDE probability, the image pixels are reassigned to the layer, whichis called the refinement step. The sampling and KDE continues until the initial layer stabilizes.The previously extracted layer is suppressed from the initial histogram and this process of layerextraction continues until a Kullback-Leibler divergence states the most recent extracted layerwas not meaningful. The rest of the images are processed using the previous layers and startingat the refinement step.Once the image set is layered from the initialization, future frames are able to be processed.In order to incorporate spatial information, a w × w × M window is used for each pixel, whereM is the the amount of previous frames being considered, and w is the amount of pixels in the xand y direction considered for spatial information. For each layer that exists in this window, theKDE is used to generate the probability that pixel p belongs to the layer, as well as the layer of14
outliers which comprises the foreground. Depending on the layer classification, the pixel is thenclassified as foreground or background.Optic flow [25] has been used for image segmentation as well. In [26], Zucchelli et al. clustermotion fields in order to recognize image planes. While these planes are not applied to foregroundsegmentation, the step is highly intuitive given the planes recovered. The use of motion in imagesegmentation has been gaining traction recently because of new hardware implementations thatare able to calculate flow in real-time. This represents an exciting avenue for future researchusing this additional feature.In [24], motion is used directly for background subtraction. Kernel Density Estimation [27] isused to model the distribution of the background using five dimensional feature vectors. Basedon this distribution classification is performed based on thresholded probabilities of instancesbelonging to the background distribution. The feature vectors consist of three dimensions fromcolor space intensities, and two from optic flow measurements (which consists of the flow measurementsand their uncertainties). This algorithm performed extremely well when compared toMixture of Gaussian models.2.3 Illumination ConsiderationsMany approaches have been used for illumination invariant background classification. Onereason for poor FG/BG segmentation in varying illumination is because most tracking systemsrely on color from RGB color space, which is highly variant to illumination changes. Becausegrayscale color space is a linear transformation from RGB the same problem exists. A commonsolution is the non-linear mapping of RGB color to another color space that is less illuminationinvariant.Many color conversions that claim to be illumination invariant have been proposed. Theoritically,the hue component of HSI color space and the luma commponent (Y) of YCbCr colorspace are illumination invariant, however in practice this is not typically the case (see Section3.3). In [24] Mittal and Paragios use a color mapping of RGB → rgI (where I = (R + G + B)/3,r = 3R/I, and g = 3G/I) due to claimed illumination invariance under certain conditions. Experimentationperformed in this work using this color space failed to observe such illuminationinvariant conditions, though this is not to say they do not exist. In [28], Gevers and Smeulders15
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 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 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

Background Subtraction Using Ensembles of Classifiers with an ...

Create successful ePaper yourself

Delete template?

Save as template?