In this article, we propose a visual pedestrian detection system which couples pedestrian appearance and pedestrian motion in a Bayesian fashion, with the goal of making detection more invariant to appearance changes. More precisely, the system couples dense appearance-based pedes-trian likelihoods derived from a sliding-window SVM detector to spatial prior distributions obtained from the prediction step of a particle filter based pedestrian tracker. This mechanism, which we term dynamic attention priors (DAP), is inspired by recent results on predictive visual attention in humans and can be implemented at negligible computational cost. We prove experimentally, using a set of public, annotated pedestrian sequences, that detection performance is improved significantly, especially in cases where pedestrians differ from the learned models, e.g., when they are too small, have an unusual pose or occur before strongly structured backgrounds. In particular, dynamic attention priors allow to use more restrictive detection thresholds without losing detections while minimizing false detections.