Ongoing human action recognition with motion capture

By Mathieu Barnachon, Saïda Bouakaz, Boubakeur S. Boufama, Erwan Guillou. 2014

131 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

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

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “A large number of skeleton-based human representations using similar histogram encoding methods were also introduced, including Histogram of Joint Position Differences (HJPD) [133], Histogram of Oriented Velocity Vectors (HOVV) [161], and Histogram of Oriented Displacements (HOD) [208], among others”
Deep Representation Learning for Human Motion Prediction and Classification (2017, methodology): “For this, we follow the experimental setup for CMU mocap action classification described in [3].”
Action recognition on motion capture data using a dynemes and forward differences representation (2014, methodology): “in [24] proposed a method that uses histograms of 3D motion capture data for action recognition.”
3D skeleton-based human action classification: A survey (2016, methodology): “The method detailed in [115] groups all poses composing an action into a set of clusters based on the Hausdorff distance.”
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset (2014, methodology): “Finally, in addition to the described validation methods that are frequently used in the literature, there are other authors that have not been included in any table because either the validation method is unclear [66,67] or the employed settings are not used by more than one author [68,69,70,71].”
Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences (2015, methodology): “[2] use a learnt vocabulary of key poses (based on a K-Means variant) and use distances between histograms of sub-actions in order to classify ongoing actions.”