  Although mobile robots have on-board sensors to perform navigation, their
efficiency in completing paths can be enhanced by planning to avoid human
interaction. Infrastructure cameras can capture human activity continuously for
the purpose of compiling activity analytics to choose efficient times and
routes. We describe a cascade temporal filtering method to efficiently extract
short- and long-term activity in two time dimensions, isochronal and
chronological, for use in global path planning and local navigation
respectively. The temporal filter has application either independently, or, if
object recognition is also required, it can be used as a pre-filter to perform
activity-gating of the more computationally expensive neural network
processing. For a testbed 32-camera network, we show how this hybrid approach
can achieve over 8 times improvement in frames per second throughput and 6.5
times reduction of system power use. We also show how the cost map of static
objects in the ROS robot software development framework is augmented with
dynamic regions determined from the temporal filter.
