Exploring discriminative pose sub-patterns for effective action classification

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3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks (2014, background): “Several compositional approaches were studied for complex scenarios and achieved substantial progresses[33, 29, 39, 21, 23, 30, 4, 15], and they decomposed an activity into deformable parts and enriched the models with contextual information.”
A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition (2015, background): “Several compositional part-based approaches have been studied for complex scenarios and achieved substantial progresses (Wang and Mori 2011; Wang et al. 2013, 2012; Zhao et al. 2013; Packer et al. 2012; Sadanand and Corso 2012; Cheng et al. 2011), and they represent an activity with the deformable p”
Skeleton based action recognition with convolutional neural network (2015, background): “Based on those coordinates, effective and efficient approaches for action recognition have been developed recently [5, 19, 26].”
Effective Active Skeleton Representation for Low Latency Human Action Recognition (2016, background): “[17], [18] can only handle segmented videos, i.”