Learning a discriminative mid-level feature for action recognition

In this paper, we address the problem of recognizing human actions from videos. Most of the existing approaches employ low-level features (e.g., local features and global features) to represent an action video. However, algorithms based on low-level features are not robust to complex environments such as cluttered background, camera movement and illumination change. Therefore, we propose a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features of densely sampled 3D cuboids. Each cuboid is classified by the corresponding random forests with a novel fusion scheme, and the cuboid's posterior probabilities of all categories are normalized to generate a histogram. After that, we obtain our mid-level feature by concatenating histograms of all the cuboids. Since a single low-level feature is not enough to capture the variations of human actions, multiple complementary low-level features (i.e., optical flow and histogram of gradient 3D features) are employed to describe 3D cuboids. Moreover, temporal context between local cuboids is exploited as another type of low-level feature. The above three low-level features (i.e., optical flow, histogram of gradient 3D features and temporal context) are effectively fused in the proposed learning framework. Finally, the mid-level feature is employed by a random forest classifier for robust action recognition. Experiments on the Weizmann, UCF sports, Ballet, and multi-view IXMAS datasets demonstrate that out mid-level feature learned from multiple low-level features can achieve a superior performance over state-of-the-art methods.