Sorting Atomic Activities for Discovering Spatio-temporal Patterns in Dynamic Scenes

We present a novel non-object centric approach for discovering activity patterns in dynamic scenes. We build on previous works on video scene understanding. We first compute simple visual cues and individuate elementary activities. Then we divide the video into clips, compute clip histograms and cluster them to discover spatio-temporal patterns. A recently proposed clustering algorithm, which uses as objective function the Earth Mover's Distance (EMD), is adopted. In this way the similarity among elementary activities is taken into account. This paper presents three crucial improvements with respect to previous works: (i) we consider a variant of EMD with a robust ground distance, (ii) clips are represented with circular histograms and an optimal bin order, reflecting the atomic activities'similarity, is automatically computed, (iii) the temporal dynamics of elementary activities is considered when clustering clips. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches.