# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 2 Related Work  
In the context of HOIR, the literature reveals a variety of models/methodologies that have significantly advanced the understanding and capability of such systems across various applications [12; 13; 14; 15; 16; 17; 18; 19]. These studies offer a foundational backdrop against which the current research on HOIR stands. A study proposed a caption generation model for the recognition of actions of sports players while utilizing an ontology-based identification scheme [20]. Researchers utilized the capabilities of graph convolutional neural networks and multiple multilayer perceptron for the identification of HOI [21; 22]. Similarly, another work used the hidden Markov model for the precise positioning of the target and recognition of human interaction [23]. Furthermore, a recent study used a hybrid model for the detection of objects while using the skeletal method [24]. The experimental results show that their proposed model had an average accuracy of 98.5% in the recognition of HOIs. A recent work stated the difficulty of organizing a dataset in which all the combinations of humans and objects are considered [25]. This process is very time consuming and requires a huge amount of computational resources. Therefore, the authors proposed an HOI detector for all new and unseen interactions. There are two types of encoder in their proposed model: one is for visual data and the other is used to detect and recognize text. Their proposed model efficiently detects HOIs while utilizing the capabilities of the layered architecture of an HOI vision transformer. Similarly, relation parsing neural network techniques are used for the detection of HOI. Two graphs are used in the proposed model to capture the relationship between objects and humans. The first graph is responsible for identifying the relationship between a person and body parts, while the second graph captures the relationship between the surrounding object and body part. Furthermore, an action-parsing machine is used for creating an association between both these graphs. The authors used the V-COCO dataset to evaluate the performance of the proposed model. Another work proposes a mechanism in which unseen data are utilized for the detection of objects [26]. The authors utilized the capabilities of an external knowledge graph for identifying the process of learning verb-noun pairs. Their proposed model not only beats state-of-the-art schemes in terms of recognition of unseen combinations but also shows promising ability in the detection of novel concepts. Moreover, researchers proposed a neural network-based mechanism that detects the motion of humans for leveraging neighboring frames, which ultimately helps in predicting occluded frames [27]. Furthermore, the authors also proposed a pose prediction network, in which object poses can be recovered even under heavy occlusions. In the literature, a study is found that used a scene graph that can recognize the semantic relationships presented in the image dataset, which ultimately helps in the identification of crucial and significant contextual information for human-object interaction inference [28]. The abovementioned literature reviews the models that use visual datasets of videos or images for HOIR. These models produce efficient recognition performance; however, they are computationally intensive. Moreover, these methods are designed to detect/recognize HOIs from images/videos generated by the standard (i.e., exocentric) camera view. However, for privacy-preserving applications, the egocentric camera view is crucial. A few studies proposed HOIR methods based on egocentric views [29; 30], which are crucial for applications in virtual reality and human-robot collaboration [31; 32]. Thus, this paper presents a method for HOIR based on the videos available from egocentric cameras using the HOI4D dataset. In this regard, we propose a framework that simplifies the process of HOI by using hand pose information.