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.

The output of the former type of recognition is a set of activities taking place in a short period of time: 'short-term activities'. The output of the latter type of recognition is a set of'long-term activities', ie predefined temporal combinations of short-term activities. We focus on high-level recognition. We define a set of long-term activities of interest, such as'fighting' and'meeting', as temporal combinations of short-term activities -- eg, 'walking', 'running', and'inactive' (standing still) -- using a logic programming implementation of the Event Calculus [9]. More precisely, we employ the Event Calculus to express the temporal constraints on a set of short-term activities that, if satisfied, lead to the recognition of a long-term activity. We presented preliminary results on activity recognition from video content in [2].