## 1 Introduction
These layers are learned from the raw data using a general purpose learning procedure which does not need to be designed manually by the experts [13]. This paper further examines various computer-based fields such as 3D games and animations systems [14; 15], physical sciences, health-related issues [16; 17; 18], natural sciences and industrial academic systems [19; 20]. Figure 3: Example of learning-based representation approach. Figure 2: Example of handcrafted representation-based approach. One of the important components of vision-based activity recognition system is the camera/sensor used for capturing the activity. The use of appropriate cameras for capturing the activity has great impact on the overall functionality of the recognition system. In fact, these cameras have been instrumental to the progression of research in the field of computer vision [21; 22; 23; 24; 25]. According to the nature and dimensionality of images captured by these cameras, they are broadly divided into two categories, i.e., 2D and 3D/depth cameras. The objects in the real world exist in 3D form: when these are captured using 2D cameras then one dimension is already lost, which causes the loss of some important information. To avoid the loss of information, researchers are motivated to use 3D cameras for capturing the activities. For the same reason, 3D-based approaches provide higher accuracy than 2D-based approaches but at higher computational cost. Recently, some efficient 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. However, these sensors also have several limitations such as these sensors only capture the frontal surfaces of the human and other objects in the scene. In addition to this, these sensors also have limited range about 6-7 m, and data can be distorted by scattered light from the reflective surfaces [26]. However, there is no universal rule for selecting the appropriate camera; it mainly depends on the nature of the problem and its requirements. A good number of survey and review papers have been published on HAR and related processes. However, due to the great amount of work about it, already published reviews are always out-of-date. For the same reason, writing a review paper on human activity recognition is hard work and a challenging task. In this paper we provide the discussion, comparison, and analysis of state-of-the-art methods of human activity recognition based on both handcrafted and learning-based action representations along with well-known datasets and important applications. This is the first review article of its kind that covers all these aspects of HAR in a single article with more recent publications. However, this review is more focused towards human gesture, and action recognition techniques, and provides little coverage regarding complex activities such as interactions and group activities. The rest of the paper is organized as follows. Handcrafted representation and recognition-based approaches are covered in Section 2, learning-based representation approaches are discussed in Section 3, Section 4 discusses the well-known public datasets and important application of HAR are presented in Section 5, discussions and conclusion are presented in Section 6.