Background Subtraction Using Ensembles of Classifiers with an ...

Table 5.1. Description of data sets usedData Set Setting Difficulty Resolution Frame Rate IlluminationOTCBVS[52] Outdoor Hard 320 x 240 Low Highly DynamicPETS 2001 [51] Outdoor Medium 768 x 576 High Slightly DynamicPETS 2006[53] Indoor Easy 720 x 576 High StaticFigure 5.1. Sample frames from OTCBVS data set3, from the 2006 conference. The third data set used is data set 03 from the OTCBVS dataset[52]. These sets each represent separate image domains as seen in Table 5.1.The OTCBVS data set offers the most difficultly due to the varying illumination caused fromcloud cover. The examples that were shown in Figure 3.3 were taken from this data set. Thesharp illumination changes caused from rolling cloud cover causes extreme variations in the RGBintensities and do not match the background distributions in the Mixture of Gaussians model.More sample frames from the OTCBVS data may been seen in Figure 5.1.As will be mentioned in Section 5.3, each data set is split into two separate sets: one fortraining to generate the optimal parameters, and one for testing. The split of the PETS 2001data sets was such that in the training set no major illumination variations were present andin the testing set a gradual, global illumination change occurred.This non-stratified split isexpected when performing sequential splits of data sets, but it causes a suboptimal performanceof all classifiers tested on the test set. One solution would be to generate the split of the testingand training sets by putting every other image in one set because this would reflect the sameillumination condition in each set. This was not done, however, because having unpredictableconditions in an image set demonstrates a greater reflection of the real world difficulties trackingsystems face.32