Background Subtraction Using Ensembles of Classifiers with an ...

More documents

Recommendations

Info

CHAPTER 5RESULTS5.1 MethodologyIn order to evaluate the results of the proposed meta-classifier, a comprehensive method ofcomparing the outputs from the classifiers must be used.Each classifier outputs a per pixelhypothesis indicating whether that pixel is a member of the foreground or some backgroundprocess. A high level tracking system would receive these pixels, cluster them into blobs, andprocess them for a higher level semantic.Classifiers in this work will be evaluated based on their ratio of true positive to false positiveclassification. Using these measurements allows for a receiver operating characteristics (ROC)graph.ROC graphs are highly expressive, and provide a visualizable comparison of multipleclassifiers’ performances. ROC graphs are more ideal with classifiers that generate a class probabilityor degree of membership [49], which the Mixture of Gaussians does by varying the βthreshold parameter. Another benefit of using ROC graphs is that the results are invariant tothe distributions of classes [49], which is not the case using metrics such as precision and recall[50].In one method of evaluating the classifier true positive results would be based on its per pixelaccuracy, however some problems exist using this method. One major problem with a per pixelevaluation criteria is that the true positive results may be misleading. Consider, for example, ascene with two foreground objects x and y that each occupy the same number of image pixels. IfClassifier A correctly classifies all the pixels of object x as foreground and incorrectly classifies allthe pixels of object y as background, then it will have 50% true positive rate for that frame. IfClassifier B correctly classifies half of the pixels in both objects then it too will have a 50% truepositive rate. However, a strong argument could be made that Classifier B is more successful.This is due to the fact that half of an object’s pixels being classified as foreground should beenough for the higher level tracking algorithm to recognize both of these objects. The output30
from Classifier A will certainly not be sufficient in recognizing both objects. In addition to thisdrawback, generating per pixel ground truth information is highly tedious and prone to error.Another method of generating true positive results is based on the number of objects correctlyfound, which is the method that will be used in this work. Using this method the ground truthwill consist of bounding box locations of the foreground objects. Because the bounding box foreach foreground object will also contain background pixels, whether or not an object is found willbe based on the percentage of pixels classified as foreground within the box.There are few choices for representations of false positives. These pixels could be grouped intoobjects and the number of false objects found would then be reported, however this metric failsto incorporate the size or shape of the objects found. Instead, a simple count of the number offalse positive pixels will be used. Of course, this will only include pixels outside of the boundingbox, ground truth regions labeled as foreground. The ROC graphs presented will list the amountof false positives in a range of 0 to 1. This will be the average number of false positives pixelsfrom each frame divided by the total area of the frame. Of course this prevents ever having thefalse positive ratio equal 1 when foreground objects are present because not all pixels can belabeled as false positive. The alternative is represent the number the false positives in a frame asthe number of false positive pixels divided by the amount of possible false positive pixels (whichwould be the total image area minus the area occupied by foreground regions). Theoretically thisis the appropriate metric, however is has the potential to falsely characterize the performance.The reason for this is that is that the false positive measure will be a function of the foregroundsize. If one frame has a low number of foreground regions then the a small number of false positivepixels will appear equally as negative as a frame with a large number of foreground regions andmany more false positive pixels. Clearly the false positive performance in the later case is worseand should be reflected when analyzing the total results over a feature set.5.2 Data SetsThree publicly available data sets are used in this work for evaluation. Two of the data sets aretaken from the International Workshop on Performance Evaluation of Tracking and Surveillance(PETS). One is data set 2 from the 2001 conference [51], and the other is from data set S3, subset31
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 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?