# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 2 Related Work  
Handcrafted action representation is the traditional approach for action recognition. Handcrafted methods are further divided into two major groups i.e., space-time-based approach and appearance-based approach. The space time interest point (STIP) detector, feature descriptor, vocabulary builder, and classifier are the four major components of space-time-based technique [10]. STIP detectors are divided into two types: dense and sparse detectors, whereas feature descriptor is further divided into local and global descriptors. Local descriptors work on colour, posture, and texture while global descriptors use speed variations, illumination changes, and phase changes of video [1]. Space-time volumes (STVs) are the features in the space-time domain which are represented as a 3D spatio-temporal cuboids. For action recognition, STVs measure similarity between volumes. Traditional methods, which involve the computation of optical flow, have some limitations that can be avoided by doing action recognition directly in the space-time volume [11; 12; 13]. For the purpose of human action recognition, the space-time features-based techniques extract unique features from space-time volumes or space-time trajectories. These aspects are generally local in their nature and contain distinguishing properties of an action. The characteristics of space-time volumes and trajectories can be categorised as either sparse or dense, depending on the nature of the space-time volumes and trajectories [3]. The feature detectors that are considered to be sparse are those that are based on interest point detectors such as Harris3D [14] and Dollar [15]. On the other hand, the feature detectors that are based on optical flow are considered to be dense. These interest point detectors have served as the foundation for the majority of the newly presented algorithms [3]. Based on the interest points that were found with Harris3D [14], the authors of [16] constructed the feature descriptor and classified the data with PCA (principal component analysis)-SVM. A novel local polynomial space-time descriptor based on optical flow was proposed by the authors in [17] for the purpose of action representation. The spatio-temporal volume of a video sequence is modelled as a three-dimensional object using shape-based approaches. The various events that occur in a video each generate their own unique shapes, and the purpose of these kinds of algorithms is to recognise an event by identifying its shape [18]. The shape of an event can be characterised using a variety of ways, such as shape invariants [11; 19], which are employed by shape-based methods. [PERSON] et al. [20] expand this method to motion-history volumes. When the action of interest is carried out in an environment that allows for reliable segmentation, these strategies function in the most optimal manner. In particular, for static scenes, methods such as background reduction can be used to build high-quality spatio-temporal volumes that are suitable to this analysis. Deep learning has developed as a prominent machine learning direction, outperforming older approaches in many computer vision applications. Deep learning algorithms have the unique capacity to learn features from raw data, eliminating the requirement for constructed feature detectors and descriptors. Deep learning models are classified into two types: unsupervised/generative models and supervised/discriminative models. Convolution neural network (CNN) is one of the most common supervised learning deep learning methods. The majority of existing learning-based representations either directly apply CNN to video frames or use CNN variations for spatio-temporal characteristics. These models have also performed well on challenging human activity recognition datasets [1]. A convolutional neural network is most typically used in deep learning to analyse visual imagery. CNN research is still ongoing and has great room for advancement. The main enhancements in CNN performance are thought to have occurred between 2015 and now.