# Human Behavior Analysis: A Survey on Action Recognition
## 2 Human Action Recognition
### Global Representations
More recently, as an emerging topic in deep learning research, generalising neural networks towards structured graph data resulted in graph convolutional networks (GCNs) [78; 79; 80; 81; 82]. Justified by its better extraction of concise features among graph structured data, GCNs have been in the scope of several works towards action recognition with skeleton data [68; 83; 84; 85; 86]. Usually, a spatiotemporal graph convolution is defined as a set of nodes and edges, where the nodes represent the skeleton joints and the edges denote the connectivity between those joints intra-frame and inter-frame. Figure 7 illustrates a GCN architecture example using skeleton data. One of the first methods to develop a spatiotemporal GCN, for human behaviour understanding, was the work by [PERSON] et al. [86] where they presented three partition strategies (neighboring). Uni-labelling gives the same vector weight to all neighbour joints; however, they can lose the local differential over the skeleton sequence. Distance Partitioning yields two weight vectors for the root node and the remaining neighbours, extracting local differential properties. At last, spatial configuration partitioning, which labels the nodes according to their distance from the gravity centre of the skeleton. Instead of using undirected graphs, where the GCN will learn its connections by itself, [PERSON] et al. [87] proposed a directed GCN in order to model the dependencies of joints and bones in the human body to extract local information better. Despite the effectiveness of considering the skeleton joints dependencies, there must have flexibility, in order to the network extract its own relevant dependencies from the skeleton features. [PERSON] et al. [88] proposed an LSTM aggregated to a GCN with the purpose of better extracting its temporal information, which they use for the selection of key joints in order to produce a soft attention mechanism. [PERSON] et al. [85] proposed a reinforcement learning [89] strategy combined with a GCN for action recognition. Their agent is responsible for extracting the most informative frames (keyframes) in order to feed the GCN more efficiently. With the applicability of the recent technique of neural architecture search (NAS) [90], [PERSON] et al. [91] proposed a dynamic GCN, where its connectivity is built upon a search space based on node correlations, achieving competitive results with its state-of-the-art approaches (Section 4). Considering the evolution of deep convolutional neural networks, [PERSON] et al. [92] introduced capsule networks as a new representation method that successfully overcame the state-of-the-art in some problems. A set of neurons composes a capsule, where its activity vector represents different features of a specific type of entity. A capsule network follows a level hierarchy, where higher-level capsules will cover more extensive regions of the image (more complex representations with more degrees of freedom) while in the counterpart, the lower-level capsules will make predictions for smaller regions of the image, with the rationale that when multiple low-level capsules achieve a prediction consensus, a higher-level capsule will become active. Inspired by the advances in capsule networks [93], [PERSON] et al. [94] proposed a capsule network analysing the 3-dimensional data in order to achieve spatiotemporal action localisation. Following a masking procedure, the capsule activations are set to 0, except for the capsule representing the ground truth class, predicting the action localisation through the largest activation and feeding a fully-connected network in order to extract a feature map for better localisation.