# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 1 Introduction  
Over the past years, automatic human activity recognition (HAR), using computer vision has raised much awareness for researchers in the whole world because it provides accurate and desired outcomes. HAR is a versatile tool in many applications. Among these the significant applications are: Human Computer Interaction (HCI), intelligent video surveillance, ambient assisted living, human-robot interaction, entertainment, and content-based video search. In HCI, when a user performs a task, that task is observed by the activity recognition systems and through feedback the user is guided to complete it. Video surveillance utilises the activity recognition system in order to automatically spot and indicate a suspicious activity to authorities for immediate action. In the same manner, in entertainment field, all the activities of players in the game are perceived by these systems. Consisting on the complexity and duration, these activities can be arranged into four categories, i.e., gestures, actions, interactions, and group activities [1]. Basic movement of the human body parts that provide some meaning is referred as a gesture. Some good examples of gestures include 'Head shaking', 'hand waving', and 'facial expression'. A type of activity performed by a single person is called action. As a matter of fact, when multiple gestures are merged together, such as 'walking' 'running', 'jogging', and 'punching', these are described as human actions. Interaction is defined as a two-actor activity. One of these two actors must be a person, and the other can either be a person (human-human interaction) or an object (human-object interaction). Group activities are the most sophisticated activities that combine gestures, movements, and interactions. It requires one or more objects and more than two people. For human action recognition, the bottom-up strategy is generally followed by the handcrafted representation-based technique. There are three major phases in general i.e., foreground detection, handcrafted feature extraction and representation, and classification [2]. Handcrafted action representation is the traditional approach for action recognition. This method is widely used in the HAR community, and it has shown impressive results on a variety of public datasets [3]. Traditional methods receive input data, extract the features from the data, and use them to train the classifier for classification purposes. Input of the system can be RGB, RGB-D, skeleton data and multi-modal [2]. Feature extraction is used for extracting the useful information from the input data which can form suitable set of data for classification of actions. HAR system's effectiveness is determined by its capacity for extracting, modelling, and expressing significant features [4]. It appears that problems with the extraction and representation of features are still being extensively researched in the fields of machine learning and computer vision [5]. The technique of extracting features, which describes patterns that are significant in the recognition process, from arbitrary input information, such as photos, videos, and text, is known as feature extraction [6]. Numerous feature-extraction techniques make use of both the low and high levels approaches to provide the required results. The recognition algorithm further fuses the cues acquired at these levels to provide qualitative results [7]. The consequences of model complexity must be taken into account because HAR is a real-time system. If the user must wait a long time to receive the result, even though the model is flawless, it will not be advantageous for the application [8]. The number of features determines the complexity of the model; for example, the computational complexity would increase as the number of features increases [6]. Currently, the HAR system using skeleton data is extracting the skeleton using OpenPose, for each detected body, a 2D skeleton with 25 joints is extracted. The 2D skeletal characteristics are then converted to RGB images.