## 1 Introduction
This is followed by a generic trainable classifier for action recognition as shown in Figure 2; (2) Learning-based representation approach, which is a recently emerged approach with capability of learning features automatically from the raw data. This eliminates the need of handcrafted feature detectors and descriptors required for action representation as in the traditional approach. Unlike the traditional handcrafted approach, it uses the concept of a trainable feature extractor followed by a trainable classifier, introducing the concept of end-to-end leaning, as shown in Figure 3.  
Figure 1: Categorization of different level of activities. The handcrafted representation-based approach mainly follows the bottom-up strategy for HAR. Generally, it consists of three major phases (foreground detection, handcrafted feature extraction and representation, and classification) as shown in Figure 4. A good number of survey papers have been published on different phases of handcrafted representation-based HAR processes. Different taxonomies have been used in the survey papers to discuss the HAR approaches. A survey presented in [1], divides the activity recognition approaches into two major categories: single layered approaches and hierarchical approaches. Single-layered approaches recognize the simple activities from the sequence of a video, while hierarchical approaches recognize more complex activities by decomposing them into simple activities (sub-events). These are further sub-categorized such as space-time volumes, and trajectories, based on the feature representation and classification methods used for recognition. A detailed survey on object segmentation techniques is presented in [2], discussing the challenges, resources, libraries and public datasets available for object segmentation. Another study, presented in [3], discussed the three levels of HAR, including core technology, HAR systems, and applications. Activity recognition systems are significantly affected by the challenges such as occlusion, anthropometry, execution rate, background clutter, and camera motion as discussed in [4]. This survey categorized the existing methods based on their abilities for handling these challenges. Based on these challenges, potential research areas were also identified in [4]. In [5], human action recognition methods based on the feature representation and classification were discussed. Similarly, [PERSON], [PERSON] et al. [6] surveyed the human activity recognition methods by categorizing them into segmentation, feature representation and classification. A review article on semantic-based human action recognition methods is presented in [7]. It presents the state-of-the-art methods for activity recognition which use semantic-based features. In this paper, semantic space, and semantic-based features such as pose, poselet, related objects, attributes, and scene context are also defined and discussed. Different handcrafted features extraction and representation methods have been proposed for human action recognition [8; 9; 10; 11; 12]. On the other hand, a learning-based representation approach, specifically deep learning, uses computational models with multiple processing layers based on representation learning with multiple levels of abstraction. This learning encompasses a set of methods that enable the machine to process the data in raw form and automatically transform it into a suitable representation needed for classification. This is what we call trainable feature extractors. This transformation process is handled at different layers, for example, an image consists of an array of pixels, and then the first layer transforms it into edges at particular location and orientation. The second layer represents it as collection of motifs by recognising the particular arrangement of edges in an image. The third layer may combine the motifs into parts and the following layers would turn it into the recognizable objects.