Background Subtraction Using Ensembles of Classifiers with an ...

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