3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding

By Ankur Gupta, Julieta Martinez, James J. Little, Robert J. Woodham. 2014

124 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “[172] 3D Pose w/o Body Parts Conc Lowlv Hand X X”
Histogram of Oriented Principal Components for Cross-View Action Recognition (2014, background): “Keywords: Spatio-temporal keypoint, pointcloud, view invariance.”
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points (2018, neutral): “2 - - nCTE [16] V 68.”
Learning a non-linear knowledge transfer model for cross-view action recognition (2015, methodology): “Thus, unlike existing cross-view action recognition techniques [10-12, 19, 21] the codebook we learn does not use the trajectory descriptors of training or test videos from IXMAS [34] or Northwestern-UCLA [33] datasets.”
Semantic Graph Convolutional Networks for 3D Human Pose Regression (2019, methodology): “Remaining works target at exploiting temporal information [11, 18, 21, 57] for 3D pose regression.”
On Human Motion Prediction Using Recurrent Neural Networks (2017, background): “wide variety of fields, such as action prediction for sociallyaware robotics [21], 3D people tracking within computer vision [13,43], motion generation for computer graphics [22] or modeling biological motion in psychology [42].”
Exploiting Temporal Information for 3D Human Pose Estimation (2017, methodology): “[35] and Chen and Ramanan [36] also used this idea of using the detected 2D pose to query a large database of exemplar poses to find the nearest nearest neighbor 3D pose.”
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction (2020, unknown): “The related techniques can be widely applied to many computer vision and robotics scenarios, such as human-computer interaction [24, 23, 17, 13], autonomous driving [6], and pedestrian tracking [1, 15, 3].”
Learning a Deep Model for Human Action Recognition from Novel Viewpoints (2016, methodology): “Dense trajectories have shown to be effective for action recognition [12], [13], [14], [15].”