  Recent work on background subtraction has shown developments on two major
fronts. In one, there has been increasing sophistication of probabilistic
models, from mixtures of Gaussians at each pixel [7], to kernel density
estimates at each pixel [1], and more recently to joint domainrange density
estimates that incorporate spatial information [6]. Another line of work has
shown the benefits of increasingly complex feature representations, including
the use of texture information, local binary patterns, and recently
scale-invariant local ternary patterns [4]. In this work, we use joint
domain-range based estimates for background and foreground scores and show that
dynamically choosing kernel variances in our kernel estimates at each
individual pixel can significantly improve results. We give a heuristic method
for selectively applying the adaptive kernel calculations which is nearly as
accurate as the full procedure but runs much faster. We combine these modeling
improvements with recently developed complex features [4] and show significant
improvements on a standard backgrounding benchmark.
