Action recognition with spatio-temporal augmented descriptor and fusion method

Action recognition is one of the most popular fields of computer vision, and lots of efforts have been made to improve recognition accuracy. While multiple descriptors are extracted to represent action, the spatio-temporal information is lost. In order to incorporate spatio-temporal information, we propose a novel method called augmented descriptor by adding the information to the original descriptor. As descriptors represent different video features, such as static appearance and motion information, previous methods just concatenate various descriptors. However, we propose a fusion method to boost the recognition accuracy of action recognition. The Multiple Kernel Learning is utilized to fuse different descriptors to get better representation in our fusion method. We also evaluate the contribution of normalization method to recognition accuracy. Our proposed methods are tested on the benchmark datasets, Olympic Sports dataset and HMDB51 dataset. The experimental results show that our approaches outperform the baseline method of improved trajectories and are effective in recognizing various actions.