'Person Runs' detection for the TRECVID Surveillance Event Detection task

We discuss an event detection approach based on local feature modeling, which is based on spatio-temporal cuboids and perspective normalization (QMUL-ACTIVA 3 / p-baseline 1). Motion information is compared against examples of events learned from a training dataset to define a similarity measure. This similarity measure is then analyzed in both space and time to identify frames containing instances of the event of interest (a person running in an airport building). Features are analyzed locally to enable the differentiation of simultaneously occurring events in different portions of an image frame. The performance is quantified on the TRECVID 2010 surveillance event detection dataset.