Simplex-Based 3D Spatio-temporal Feature Description for Action Recognition

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Histogram of Oriented Principal Components for Cross-View Action Recognition (2014, methodology): “Finally, we propose a global descriptor that is extracted from the normalized spatio-temporal distribution of STKs in 4-D. STK-Distribution and local HOPC descriptors were evaluated for cross-view human action recognition on the Northwestern-UCLA and UWA3D Multiview Activity II datasets.”
Simultaneous Feature and Body-Part Learning for real-time robot awareness of human behaviors (2017, background): “…action understanding has been researched in robotics and computer vision communities, most previous techniques are based on local spatio-temporal visual features [1], [2], which are generally incapable of dealing with the challenges introduced by robotics applications (e.g., real-time performance).”
Detecting the Moment of Completion: Temporal Models for Localising Action Completion (2017, methodology): “For example, [6,7,8,9,10,11,12,13] extract space-time features from interest points or dense trajectories, encode them using BoWs or FVs, and then use SVM to recognise the actions.”
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks (2015, result): “9 percent, outperforming many of the compared approaches [2], [11], [64], [65], [66], except a”