Abstract— Although mobile robots have on-board sensors to  perform navigation, path planning can benefit from fixed, or  infrastructure, cameras that capture activity analytics  continuously and from non-robot perspectives. We describe an approach that collects statistics of motion that repeat at some  period (usually a day) and perform path planning to avoid  expected activity in time and space The same statistics are used to learn preferred human paths and plan robot paths on these at  times of low human activity. Temporal filtering is performed in  cascade to efficiently extract long- and short-term activity in two time dimensions (isochronal and chronological) for use in global  and local path planning. We compare our lightweight activity  detection approach to neural network object detection methods  and propose an activity-gated approach that combines activity and  object detection efficiently. We deployed our approach in the ROS  robot software development framework by augmenting the cost  map of static objects with dynamic regions determined from  activity. We describe benefits and constraints of this combination.