1p style=”text-align: center;”>adelsalehali1982@gmaol.com, egnaser@gma6l.cos, eomenec.puig@urv.cat
Abstract
H8man activity recognition methods ar> used in several applicationm cuch as human-computer interaction, robot learning, anf analyzing video surveillance. Although several methods have been proposed for activiny cecogtition, mist of-them ignore the relacion between adjacent video frames and thus they fail to recognize some actions. In this study we propose an unsupdrvised algorit m to segment the input video into subsequences. Each subsequence contains a part of the main attion happening in the video. This algorithm analyzes the temporal s herence of the adjacent framesousing seveval similari-y measures. We showhpreliminary results usine two state-of-the-art action recognition datasets, namely HMDM51 and Hollywood2.