Background Subtraction Using Ensembles of Classifiers with an ...

also different than using an unanimous vote scheme for classifier fusion in the ensemble classifierpresented in this work. This is because if only a few features match the new instance the classifierswill update themselves based on a match in the ensemble algorithm, while for the single featurevector algorithm the update would not be based on a match unless every dimension was matched(where matching is being with 2.5σ).Evaluation of the ensemble algorithm’s performance could be performed using the VACE evaluationframework in [57]. This framework offers a more comprehensive analysis of the algorithmsrobustness, as well as a comparison against the performance of other tracking algorithms.Real-time implementation of this system would require a system with multiple processingcores and a large sized main memory, but otherwise its realization is believed to be reasonable.Enhanced engineering of the algorithms to exploit the inherent parallelism of the ensemble shouldallow for the handling of relatively high frame rates depending on the image resolution.6.3 Final ThoughtsIt is believed that further research needs to be conducted in order to further understand thegains that were observed in this work. There was a clearly demonstrated advantage to usingadditional features in background classification, even though many of these features are verypoor when used individually. Through greater investigation a more clearly defined set of optimalensemble features would allow for a more general background classification algorithm that couldhave key impacts in defense, robotics, and surveillance applications.56