Abstract

This paper presents a novel framework for human action recognition based on sparse coding. We introduce an effective coding scheme to aggregate low-level descriptors into the super descriptor vec- tor (SDV). In order to incorporate the spatio-temporal information, we propose a novel approach of super location vector (SLV) to model the space-time locations of local interest points in a much more compact way compared to the spatio-temporal pyramid representations. SDV and SLV are in the end combined as the super sparse coding vector (SSCV) which jointly models the motion, appearance, and location cues. This repre- sentation is computationally efficient and yields superior performance while using linear classifiers. In the extensive experiments, our approach significantly outperforms the state-of-the-art results on the two public benchmark datasets, i.e., HMDB51 and YouTube.