Understanding the Gap between 2D and 3D Skeleton-Based Action Recognition

Large volumes of RGB video data are recorded and processed every day. One of the embedded data modality within these videos is the information about human motions. Up to now, this information has been almost unfeasible to extract, and thus human-motion understanding research has been mainly limited to 3D skeleton data captured by dedicated hardware only. However, with recent advances in computer vision, it is possible to estimate 2D skeleton sequences from ordinary videos quite accurately. Such 2D skeleton data possess an excellent potential for future motion understanding applications. In this paper, we adopt a state-of-the-art bidirectional LSTM network to analyze the accuracy gap in the expressive power of 2D and 3D skeleton data recorded simultaneously on a high number of 20k human actions. We further examine how the missing depth information and fluctuations in 2D skeleton sizes influence the recognition rate. We also demonstrate the suitability of 2D skeleton data for general daily activity recognition by reporting baselines on the PKU-MMD dataset.