A novel hierarchical framework for human action recognition

By Hongzhao Chen, Guijin Wang, Jing-Hao Xue, Li He. 2016

71 citations recorded by the source corpus.

In this paper, we propose a novel two-level hierarchical framework for three-dimensional (3D) skeleton-based action recognition, in order to tackle the challenges of high intra-class variance, movement speed variability and high computational costs of action recognition. In the first level, a new part-based clustering module is proposed. In this module, we introduce a part-based five-dimensional (5D) feature vector to explore the most relevant joints of body parts in each action sequence, upon which action sequences are automatically clustered and the high intra-class variance is mitigated. In the second level, there are two modules, motion feature extraction and action graphs. In the module of motion feature extraction, we utilize the cluster-relevant joints only and present a new statistical principle to decide the time scale of motion features, to reduce computational costs and adapt to variable movement speed. In the action graphs module, we exploit these 3D skeleton-based motion features to build action graphs, and devise a new score function based on maximum-likelihood estimation for action graph-based recognition. Experiments on the Microsoft Research Action3D dataset and the University of Texas Kinect Action dataset demonstrate that our method is superior or at least comparable to other state-of-the-art methods, achieving 95.56% recognition rate on the former dataset and 95.96% on the latter one. HighlightsWe propose a hierarchical framework for 3D skeleton-based action recognition.We introduce a part-based feature vector to automatically cluster action sequences.We present a statistical principle to decide the time scale of motion features.Our method outperforms other state-of-the-art methods on the MSRAction3D dataset.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Previous works [30], [93] have shown that the importance degrees of the features from different body parts are not the same in analyzing different actions.”
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks (2017, background): “Several previous works [13], [50] have shown that in each action sequence, there is often a subset of informative joints which are important as they contribute much more to action analysis, while the remaining ones may be irrelevant (or even”
Global Context-Aware Attention LSTM Networks for 3D Action Recognition (2017, background): “Previous works [20, 6] have already shown that in each action sequence, there is often a subset of informative joints which are important as they contribute much more to action analysis, while the other ones can be irrelevant (or even noisy) to this action.”
Fusing shape and motion matrices for view invariant action recognition using 3D skeletons (2017, unknown): “36% Two-level hierarchical framecork [27] 95.”
A Review on Human Activity Recognition Using Vision-Based Method (2017, background): “Current approaches often solve it by combining other features that Second, an intuitive fact can be observed that not all skeletal joints are involved in a particular activity, and only a few active joints are meaningful and informative for a certain activity [81].”