This paper describes a novel approach to optimal kernel placement in kernel-based tracking. If kernels are placed at arbitrary places, kernel-based methods are likely to be trapped in ill-conditioned locations, which prevents the reliable recovery of the motion parameters and jeopardizes the tracking performance. The theoretical analysis presented in this paper indicates that the optimal kernel placement can be evaluated based on a closed-form criterion, and achieved efficiently by a novel gradient-based algorithm. Based on that, new methods for temporal-stable multiple kernel placement and scale-invariant kernel placement are proposed. These new theoretical results and new algorithms greatly advance the study of kernel-based tracking in both theory and practice. Extensive real-time experimental results demonstrate the improved tracking reliability.