Human Action Recognition by Negative Space Analysis

We propose a novel region-based method to recognize human actions by analyzing regions surrounding the human body, termed as negative space according to art theory, whereas other region-based approaches work with silhouette of the human body. We find that negative space provides sufficient information to describe each pose. It can also overcome some limitations of silhouette based methods such as leaks or holes in the silhouette. Each negative space can be approximately represented by simple shapes, resulting in computationally inexpensive feature description that supports fast and accurate action recognition. The proposed system has obtained 100% accuracy on the Weizmann human action dataset and is found more robust with respect to partial occlusion, shadow, noisy segmentation and non-rigid deformation of actions than other methods.