Learning a sparse dictionary of video structure for activity modeling
We present an approach which incorporates spatiotemporal features as well as the relationships between them, into a sparse dictionary learning framework for activity recognition. We propose that the dictionary learning framework can be adapted to learning complex relationships between features in an unsupervised manner. From a set of training videos, a dictionary is learned for individual features, as well as the relationships between them using a stacked predictive sparse decomposition framework. This combined dictionary provides a representation of the structure of the video and is spatio-temporally pooled in a local manner to obtain descriptors. The descriptors are then combined using a multiple kernel learning framework to design classifiers. Experiments have been conducted on two popular activity recognition datasets to demonstrate the superior performance of our approach on single person as well as multi-person activities.