Bio-inspired predictive orientation decomposition of skeleton trajectories for real-time human activity prediction

26 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Zhang and Parker [213] implemented a bio-inspired predictive orientation decomposition (BIPOD) using mid-level features to construct representations of people from 3D skeleton trajectories, which is inspired by biological research in human anatomy.”
Simultaneous Feature and Body-Part Learning for real-time robot awareness of human behaviors (2017, methodology): “Because these mid-level body part models can partially take into account the physical structure of human body, they can yield improved discrimination power to represent humans [5].”