Action Recognition Using Super Sparse Coding Vector With Spatio-Temporal Awareness

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 vector (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 representation 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.