HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition
This addresses viewpoint robustness in 3D action recognition for applications like surveillance or human-computer interaction, representing a novel method for a known bottleneck.
The paper tackles the problem of 3D action recognition being sensitive to viewpoint variations by proposing a new technique that directly processes pointclouds, resulting in a descriptor and keypoint detector that outperform state-of-the-art algorithms on three benchmark datasets.
Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.