Spatio-temporal information for human action recognition

Human activity recognition in videos is important for content-based videos indexing, intelligent monitoring, human-machine interaction, and virtual reality. This paper uses the low-level feature-based framework for human activity recognition which includes feature extraction and descriptor computing, early multi-feature fusion, video representation, and classification. This paper improves the first two steps. We propose a spatio-temporal bigraph-based multi-feature fusion algorithm to capture the useful visual information for recognition. Meanwhile, we introduce a compressed spatio-temporal video representation to bag of words representation. Our experiments on two popular datasets show efficient performance.