# Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control
## 1 Introduction
### Related Work  
Over the last three decades, methods to estimate the pose of the head have received increasing attention [3] because of their application in various image analysis tasks. A variety of implementations can be named, ranging from, at the coarsest level, the identification of a head pose from a finite set of orientations [4] (e.g., frontal vs. lateral view) to the identification of continuous angular measurement [5,6].  
A wide variety of approaches to the estimation of head pose can be named [3], for example, 2 DOF appearance-based methods [7], geometric methods [8], tracking methods [9], multitask methods [10], embedding methods, regression methods [11], and model-based 3 DOF head registration methods [12]. It is a challenging task to classify all the approaches presented in the past. Nevertheless, in the most recent decade, an obvious shift in methodology can be observed, from traditional methods (including classic machine learning) towards deep learning [13].  
Figure 1: Simplified schematic of the proposed system. Image data captured by a webcam on the operator’s side of the system is evaluated in a coarse-to-fine manner via two independent neural network architectures. The head orientation in vertical and horizontal planes is used to calculate the corresponding control action fed to the CCTV camera mounting.  
An increasing interest in the application of CNN to head pose estimation is linked to the emergence of publicly available databases of annotated face images. They range from extensive databases, for example, VGGFace2 [14] with 9131 participants or AFLW [15] with 5749 participants, to smaller databases, for example, SASE [16] with 55 participants or The FacePix [17] with 30 participants.  
An interesting alternative is a large-scale SynHead synthetic database [18], which contains 10 synthetic head models. Synthetic data can be combined with real-life data during training. The head pose estimation is then performed from a single image. The approach presented in [19] improves the accuracy of the estimation of head poses for real-world images despite only using labels from synthetic images.  
The publicly available databases mentioned above contain images in which the direction of a person's gaze is not pointed toward the camera [14]. The estimation of head pose is strongly related to the estimation of the direction of gaze of a person [2]. Therefore, the use of the database is limited to scenarios where a person is looking in a different direction than is given by their head orientation, e.g., for surveillance operators. The need for a new database tailored for surveillance-oriented applications arises.  
As a side note, plenty of work has been done for artificial intelligence in anomaly recognition systems [20] for security applications. These systems are designed to automatically detect and take into account signs of offensive or disruptive activities immediately. Note that the problem tackled in this work is not among those considerations. The aim is to improve the overall quality of life of the disabled, the surveillance application being purely an example. Extending the presented idea can lead to the design of an interface suitable for disabled users [21].