SNN-LDS: Spatio-temporal non-negative sparse coding for human action recognition

Abstract

Current state-of-the-art approaches for visual human action recognition focus on complex local spatio-temporal descriptors, while the spatio-temporal relations between the descriptors are discarded. These bag-of-features (BOF) based methods come with the disadvantage of limited descriptive power, because class-specific mid- and large-scale spatio-temporal information, such as body pose sequences, cannot be represented. To overcome this restriction, we propose sparse non-negative linear dynamical systems (sNN-LDS) as a dynamic, parts-based, spatio-temporal representation of local descriptors. We provide novel learning rules based on sparse non-negative matrix factorization (sNMF) to simultaneously learn both the parts as well as their transitions. On the challenging UCF-Sports dataset our sNN-LDS combined with simple local features is competitive with state-of-the-art BOF-SVM methods. © 2014 Springer International Publishing Switzerland.