Weighted voting of multi-stream convolutional neural networks for video-based action recognition using optical flow rhythms.

Two of the most important premises of an ensemble are the diversity of its components and how to combine their votes. In this paper, we propose a multi-stream architecture based on the weighted voting of convolutional neural networks to deal with the problem of recognizing human actions in videos. A major challenge is how to include temporal aspects into this kind of approach. A key step in this direction is the selection of features that characterize the complexity of human actions in time. In this context, we propose a new stream, Optical Flow Rhythm, besides using other streams for diversity. To combine the streams, a voting system based on a new weighted average fusion method is introduced. In this scheme, the weights of classifiers are defined by an optimization process led by a metaheuristic. Experiments conducted on the UCF101 and HMDB51 datasets demonstrate that our method is comparable to state-of-the-art approaches.