Action recognition using spatial-optical data organization and sequential learning framework

Recognizing human actions in videos is a challenging problem owning to complex motion appearance, various backgrounds and semantic gap between low-level features and high-level semantics. Existing methods have scored some achievements and many new thoughts have been proposed for action recognition. They focus on designing a robust feature description and training an elaborate learning model, and many of them can benefit from a two-stream network with a stack of RGB frames and optical flow frames. However, these features for human action representation are struggling with the limited feature representation as RGB videos are confused by static appearance redundancy and optical flow videos cannot represent the detailed appearance. To solve these problems, we propose an efficient algorithm based on the spatial-optical data organization and the sequential learning framework. There are two contributions of our method: a novel data organization based on hierarchical weighting segmentation and optical flow for video representation, and a lightweight deep learning model based on the Convolutional 3D (C3D) network and the Recurrent Neural Network (RNN) for complicated action recognition. The new data organization aggregates the merits of motion appearance, movement trajectories and optical flow in a creative way to highlight the meaningful information. And the proposed lightweight model has an insight into patterns and semantics of sequential data by low-level spatiotemporal feature extraction and high-level information mining. The proposed method is evaluated on the state-of-the-art dataset and the results demonstrate that our method have a good performance for complex human action recognition.