Real Time Human Action Recognition in a Long Video Sequence

In recent years, most action recognition researches focus on isolated action analysis for short videos, but ignore the issue of continuous action recognition for a long video sequence in real time. This paper proposes a novel approach for human action recognition in a video sequence with whatever length, which, unlike previous works,requires no annotations and no pre-temporal-segmentations.Based on the bag of words representation and the probabilistic Latent Semantic Analysis (pLSA) model, there cognition process goes frame by frame and the decision updates from time to time. Experimental results show that this approach is effective to recognize both isolated actions and continuous actions no matter how long a video sequence is. This is very useful for real time applications like video surveillance. Besides, we also test our approach for real time temporal video segmentation and real time keyframe extraction.