Background Subtraction Using Ensembles of Classifiers with an ...

features false positives and a high overlap in their true positives, resulting in strong backgroundclassification.It was observed in the PETS 2001 data set and the OTCBVS data set that the performanceof classification using only the gradient magnitude feature was more successful than using thebaseline RGB. Similar results were not observed in the PETS 2006 data set. It is believed thatthe reason this occured is that the foreground objects in the PETS 2001 and OTCBVS datasets are much farther from the camera than they are in the PETS 2006 data set. This resultsin lower resolutions for the foreground objects, which in turn implies less homogeneous regionsover the space occupied by the foreground object. The final implication is that edge featuresare more prominent on the low resolution objects. This may be observed in Figure 5.19, wherean object from the PETS 2006 and OTCBVS data set are shown as well as their correspondinggradient magnitude image. In the low resolution object it has distinct edge features over its entiresilhouette, while in the high resolution object these features are only prominent on the boundariesof the object and not in the homogeneous regions. A conclusion from this observation is thatthe use of gradient magnitude in background classification of smaller objects, or objects are fardistances, is superior to the RGB features.Due to the heightened performance of the gradient magnitude in the PETS 2001 and OTCBVSdata sets, a comparison of the performance against the extended feature set was performed. TheROC comparisons can be seen in Figure 5.20. In the OTCBVS data set, the use of all featuresresults in far more accurate classification than only using the gradient magnitude. The extendedfeature set also outperforms the gradient magnitude in the PETS 2001 data set, though thereis less significant of an improvement. Because of the consistent outperformance of the extendedfeature set over using only the gradient magnitude, it recomended to use all the features for moreaccurate classification.If computational demands are a limitation in a system observing lowresolution objects then using only the gradient magnitude is an adequate alternative.48