## 1 Introduction  
In recent years, automatic human-activity recognition (HAR) based on computer vision has drawn much attention of researchers around the globe due to its promising results. The major applications of HAR include; human-computer interaction (HCI), intelligent video surveillance, ambient assisted living, human-robot interaction, entertainment, video indexing, and others [1]. Depending on the complexity and duration, human activities can be categorized into four levels: gestures, actions, interactions, and group activities. Gestures are represented by movement of the person's body parts, such as stretching an arm; actions are single-person activities such as walking, punching, kicking, running and so forth; interactions are activities of two or more than two persons, such as two person fighting with each other; and a group having a meeting is an example of group activity [2]. A large amount of work has already been done for human action recognition, but it is still a challenging problem. The major challenges and issues in HAR are as follows: (1) occlusion; (2) variation in human appearance, shape, and clothes; (3) cluttered backgrounds; (4) stationary or moving cameras; (5) different illumination conditions; and (6) viewpoint variations. Among these challenges, viewpoint variation is one of the major problems in HAR since most of the approaches for human activity classification are view-dependent and can recognize the activity from one fixed view captured by a single camera. These approaches are supposed to have the same camera view during training and testing. This condition cannot be maintained in real world application scenarios. Moreover, if this condition is not met, their accuracy decreases drastically because the same actions look quite different when captured from different viewpoints [3]. A single camera-based approach also fails to recognize the action when an actor is occluded by an object or when some parts of the action are hidden due to unavoidable self-occlusion. To avoid these issues and get the complete picture of an action, more than one camera is used to capture the action--this is known as action recognition from multiple views or view-invariant action recognition [4]. There are two major approaches for recognition of human actions from multiviews: 3D approach and 2D approach [5]. In the first approach, a 3D model of a human body is constructed from multiple views and motion representation is formed from it for action recognition. This model can be based on cylinders, ellipsoids, visual hulls generated from silhouettes, or surface mesh. Some examples of motion representation are 3D optical flow [6], shape histogram [7], motion history volume [8], 3D body skeleton [9], and spatiotemporal motion patterns [10]. Usually, the 3D approach provides higher accuracy than a 2D approach but at higher computational cost, which makes it less applicable for real time applications. In addition to this, it is difficult to reconstruct a good-quality 3D model because it depends on the quality of extracted features or silhouettes of different views. Hence, the model is exposed to deficiencies which might have occurred due to segmentation errors in each viewpoint. Moreover, a good 3D model of different views can only be constructed when the views overlap. Therefore, a sufficient number of viewpoints have to be available to reconstruct a 3D model. However, recently some 3D cameras have been introduced for capturing images in 3D form. Among these, 3D time-of-flight (ToF) cameras and Microsoft Kinect have become very popular for 3D imaging. These devices overcome the difficulties that are faced by the classical 3D multiview action-recognition approaches when reconstructing a 3D model. However, these sensors also have several limitations. For example, in contrast to a fully reconstructed 3D model from multiple views, these sensors only capture the frontal surfaces of the human and other objects in the scene.