Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition
Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. However, the diverse nature of human actions and the noisy nature of most video content make it difficult to achieve effective human action recognition. To overcome the aforementioned problems, Sparse Representation (SR) has recently attracted substantial research attention. However, although SR-based approaches have proven to be reasonably effective, the computational complexity of the testing stage prohibits their usage by applications requiring support for real-time operation and a vast number of human action classes. In this paper, we propose a novel method for human action recognition, leveraging coarse-to-fine sparse representations that have been obtained through dictionary sub-sampling. Comparative experimental results obtained for the UCF50 dataset demonstrate that the proposed method is able to achieve efficient human action recognition, at no substantial loss in recognition accuracy.