Recent work on background subtraction has shown de- velopments on two major fronts. In one, there has been increasing sophistication of probabilistic models, from mix- tures of Gaussians at each pixel [ 7 ], to kernel density esti- mates at each pixel [ 1 ], and more recently to joint domain- range density estimates that incorporate spatial informa- tion [ 6 ]. Another line of work has shown the benefits of increasingly complex feature representations, including the use of texture information, local binary patterns, and re- cently scale-invariant local ternary patterns [ 4 ]. In this work, we use joint domain-range based estimates for back- ground 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 ker- nel calculations which is nearly as accurate as the full pro- cedure but runs much faster. We combine these modeling improvements with recently developed complex features [ 4 ] and show significant improvements on a standard back- grounding benchmark.