# Complex Human-Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach
## 2 Related Works  
In the field of computer vision research, various approaches have been proposed to solve the issue of recognizing complex human daily activities. This section describes some related research into the recognition of human-object interaction. The authors of paper [4] proposed the anticipation of human intentional actions using the affordance of objects and the context of scenes for visualizing possible future actions. The experiments were performed using a Sez3D sensor. This system can predict future action when the frame observation range is between 30% and 60% of the whole action. Moreover, the authors of paper [5], proposed a system of robotic assistants that can anticipate what humans will do next using observations of pose and the surrounding environment, with the purpose of helping people with reactive response. Future actions are represented using the anticipatory temporal conditional random field (ATCRF), which is a model that can maintain a rich context of spatio-temporal relations via object affordances. Alternatively, the authors of paper [6] presented a method for predicting future actions from partially observed RGB-D (red, green, blue, depth) videos. Because of the rich context between humans and the environment while performing actions, the authors used a stochastic grammar model in order to capture the compositional structure of events and to integrate human actions with the corresponding objects and their affordances. In addition, the human-object-object (HOO) interaction affordance learning approach for improving the reliability of object recognition has also been proposed [7]. The relationship between a pair of objects is represented by a Bayesian network, which is then trained for the purpose of improving the reliability of object recognition. Moreover, a system using deep learning based on the affordance model has been proposed in [8] for recognizing human intentions and recommending objects for use. The action-object affordances were modeled using deep structure and gaze information obtained from a Tobii 1750 eye-tracker. This system is used to recognize human intentions and suggest the objects considered useful for the recognized intention. Moreover, [PERSON] et al. proposed research on learning human activities and object affordances from RGB-D videos using a structural support vector machine (SSVM) approach [9]. However, the proposed method only achieved an accuracy of 75.0% for high-level activity recognition. In subsequent research by [PERSON] et al., the spatio-temporal relationship between human poses and objects was modeled using a conditional random field for anticipating activities [10]. In the latter study, the accuracy of detecting high-level activities portrayed in the CAD-120 dataset increased to 83.1%, which is still lower than with our proposed system. In addition, the authors in [11] proposeda two-layer SVM hidden conditional random field (HCRF) recognition model for recognizing daily activities, specifically for those involving human-object interaction. However, this method relies on learning sub-activities based on the temporal sub-structure of the interaction for recognizing the high-level activities using a hierarchical SVM-HCRF model. Last but not least, a long short-term memory network was developed for recognizing the behavior of baseball players [12]. This network features the fusion of data from multiple sensors. Some researchers have applied deep learning for recognizing single person actions, such as walking, sitting, standing, etc. [PERSON] et al. [13] introduced a method for recognizing human actions by utilizing deep learning of spatio-temporal features. In this work, the authors extended a 2D convolutional neural network architecture to a 3D convolutional neural network (3D-ConvNet) architecture by adding the temporal dimension. Moreover, [PERSON] et al. [14] proposed an approach that can be directly applied to raw depth video sequences for extracting spatio-temporal features using a support vector machine (SVM) for the classification of actions.