Earth mover's prototypes: A convex learning approach for discovering activity patterns in dynamic scenes

We present a novel approach for automatically discovering spatio-temporal patterns in complex dynamic scenes. Similarly to recent non-object centric methods, we use low level visual cues to detect atomic activities and then construct clip histograms. Differently from previous works, we formulate the task of discovering high level activity patterns as a prototype learning problem where the correlation among atomic activities is explicitly taken into account when grouping clip histograms. Interestingly at the core of our approach there is a convex optimization problem which allows us to efficiently extract patterns at multiple levels of detail. The effectiveness of our method is demonstrated on publicly available datasets.