## 2 Related Works  
This section presents state-of-the-art methods for multiview action recognition based on a 2D approach. These methods extract features from 2D image frames of all available views and combine these features for action recognition. Then, classifier is trained using all these viewpoints. After training the classifier, some methods use all viewpoints for classification [18], while others use a single viewpoint for classification of a query action [19; 20; 21]. In both cases, the query view is part of the training data. However, if the query view is different than the learned views, this is known as cross-view action recognition. This is even more challenging than the multiview action recognition [22; 23].  
Different types of features--such as motion features, shape features, or combination of motion- and shape-based features--have been used for multiview action recognition. In [20], silhouette-based features were acquired from five synchronized and calibrated cameras. The action recognition from multiple views was performed by computing the \(R\) transform of the silhouette surfaces and manifold learning. In [24], contour points of the human silhouette were used for pose representation, and multiview action recognition was achieved by the arrangements of multiview key poses. Another silhouette-based method was proposed in [25] for action recognition from multiple views; this method used contour points of the silhouette and radial scheme for pose representation. Then, model fusion of multiple camera streams was used to build the bag of key poses, which worked as a dictionary for known poses and helped to convert training sequences into key poses for a sequence-matching algorithm. In [13], a view-invariant recognition method was proposed, which extracted the uniform rotation-invariant local binary patterns (LBP) and contour-based pose features from the silhouette. The classification was performed using a multiclass support vector machine. In [26], scale-invariant features were extracted from the silhouette and clustered to build the key poses. Finally, classification was done using a weighted voting scheme.  
An optical flow and silhouette-based features were used for view-invariant action recognition in [27], and principal component analysis (PCA) was used for reducing the dimensionality of the data. In [28], coarse silhouette features, radial grid-based features and motion features were used for multiview action recognition. Another method for viewpoint changes and occlusion-handling was proposed in [19]. This method used histogram of oriented gradients (HOG) features with local partitioning, and obtained the final results by fusing the results of the local classifiers. A novel motion descriptor based on motion direction and histogram of motion intensity was proposed in [29] for multiview action recognition followed by a support vector machine used as a classifier. Another method based on 2D motion templates, motion history images, and histogram of oriented gradients was proposed in [30]. A hybrid CNN-HMM model which combines convolution neural networks (CNN) with hidden Markov model (HMM) was used for action classification [17]. In this method, the CNN was used to learn the effective and robust features directly from the raw data, and HMM was used to learn the statistical dependencies over the contiguous subactions and conclude the action sequences.