Action proposals using hierarchical clustering of super-trajectories
Action localization aims to determine the spatial and temporal location of certain action which appears in a video. To facilitate action localization, spatio-temporal proposals which are likely to contain the action of interest are extracted to reduce the search space of candidate locations in a video, inspired by the object proposals in images. In this paper, considering the effectiveness of spatio-temporal trajectories for video action recognition and action proposal generation, we build our unsupervised action proposal generation pipeline upon super-trajectories. Specifically, we first group trajectories into super-trajectories inspired by super-voxels, and then employ hierarchical clustering on super-trajectories by taking different aspect and temporal ratios into consideration. Comprehensive experiments on two benchmark datasets (i.e., UCF-sports and MSR-II) demonstrate that our action proposal generation pipeline not only achieves the state-of-the-art recall, but also achieves competitive results for the action localization task.