Title: Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians,
Abstract: Background-Foreground classification is a fundamental well-studied problem in
computer vision. Due to the pixel-wise nature of modeling and processing in the
algorithm, it is usually difficult to satisfy real-time constraints. There is a
trade-off between the speed (because of model complexity) and accuracy.
Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
Gaussian Mixture Model (GMM) into an adaptive cascade of classifiers. This way
we achieve a good improvement in speed without compensating for accuracy. In
the training phase, we learn multiple KDEs for different durations to be used
as strong prior distribution and detect probable oscillating pixels which
usually results in misclassifications. We propose a confidence measure for the
classifier based on temporal consistency and the prior distribution. The
confidence measure thus derived is used to adapt the learning rate and the
thresholds of the model, to improve accuracy. The confidence measure is also
employed to perform temporal and spatial sampling in a principled way. We
demonstrate a speed-up factor of 5x to 10x and 17 percent average improvement
in accuracy over several standard videos.