Combining nonuniform sampling, hybrid super vector, and random forest with discriminative decision trees for action recognition

Trajectory-based features have become popular for action recognition and achieve the state-of-the-art results on a variety of datasets. In this paper, we propose a novel framework to improve the performance of action recognition. Specifically, we first apply the nonuniform sampling method to efficiently select features for given actions. The proposed hybrid super vector, namely fisher vector (FV) combined with vector of locally aggregated descriptors (VLAD), is then employed to encode sampled trajectories. A random forest with discriminative decision trees, where every tree node is a discriminative classifier, is finally applied to predict action labels. We have achieved 88.2% in average accuracy on the UCF101 dataset, which outperforms the best results that have been reported in the literature.