Real Time Human Action Recognition in a Long Video Sequence

Abstract

In recent years, most action recognition researches focuson isolated action analysis for short videos, but ignore theissue of continuous action recognition for a long videosequence in real time. This paper proposes a novelapproach for human action recognition in a video sequencewith whatever length, which, unlike previous works,requires no annotations and no pre-temporal-segmentations.Based on the bag of words representation and theprobabilistic Latent Semantic Analysis (pLSA) model, therecognition process goes frame by frame and the decisionupdates from time to time. Experimental results show thatthis approach is effective to recognize both isolated actionsand continuous actions no matter how long a videosequence is. This is very useful for real time applicationslike video surveillance. Besides, we also test our approachfor real time temporal video segmentation and real time keyframe extraction.