Background Subtraction Using Ensembles of Classifiers with an ...

efficient classifier when being restricted to low resolution images of the objects being tracked,using gradient magnitude as a single feature will offer satisfactory performance gains over RGBfeatures.6.2 Future WorkThe optimistic results generated opens the door for many avenues of future research in usingensembles of background classifiers.While significant improvements were shown using Haar features, as well as gradient features,incorporating additional image processing features may allow for a further improvement. Onefeature that would be intuitive to incorporate is texture. Per pixel texture features using Gaborfilters as described in [54], [55] would offer a similar, but distinctly different, representation ofa pixel than Haar features.Additional features that could be included are the Laplacian ofGaussian, non-thresholded SIFT features [56], depth, and motion features.Another direction is the fusion of different classifier algorithms. For example, instead of usinga series of Mixture of Gaussian classifiers, an ensemble of Mixture of Gaussian, Kalman Filter,and other classifiers could be used. This approach was the first direction this work researched.The major stopping point was the lack of distinct classifiers for background subtraction.Insteadof fusing strong classifier algorithms, using a series of weak algorithms should offer similarimprovements to using an extended feature set.An extended feature set could also be used without having multiple classifiers. Because theMixture of Gaussians algorithm normally operates on a feature vector (where the three featuresare RGB), using the same features in this paper as a single vector for one classifier should produceunique results. It may not be the case that significantly different results are yielded with thisapproach unless a full covariance matrix is computed, which is generally not the case. Insteadit is generally the case that only the diagonal of the covariance matrix is calculated in order toreduce computational demands. Another consideration when using a vector of features insteadof an ensemble of classifiers is that if a single dimension of new instance vector does not fallwithin 2.5σ of its corresponding dimension, and every other vector dimension does, then theinstance does not match the distribution. With a larger feature set it is often the case that notevery feature matches its corresponding dimension for its feature in the model. This approach is55