# Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control
## 4 Discussion  
In this work, a complex solution was proposed that allows the control of a CCTV camera for disabled people. The system estimates the user's head orientation on image frames acquired by the commonly available web camera. Several CNN estimators are used, which work in a coarse-to-fine manner. The first estimates the head position and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural network** & Our proposition & MTCNN [23] & OpenCV’s DNN [25] \\ \hline
**Average processing time** & 243.87 ms & 1672.3 ms & 1666.6 ms \\ \hline
**Accuracy** & 75.27\% & 93.61\% & 72.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of an experimental comparison of accuracy and time needed to process a single image frame for several different algorithms. Figure 9: Estimated head orientation, corresponding control signals, and the resulting angle change of the CCTV camera. The left column represents the movement in the horizontal direction, whereas the second—vertical. (**a**) Estimated orientation (horizontal). (**b**) Estimated orientation (vertical). (**c**) Control signals (horizontal). (**d**) Control signals (vertical). (**e**) Servomechanism angle (horizontal). (**f**) Servomechanism angle (vertical). denotes its bounding box in the image. The second determines the head orientation output class. Separate estimators are used for horizontal and vertical movements, assigning one of the five classes each. The resulting class is used to generate a control signal for the servomechanisms attached to the surveillance camera mount. To accurately train CNN estimators, the data set reflecting the final application was collected. In each image, a person looked in the direction of the camera, while the orientation of the head differed between the pictures. The range of head tilt and turn was limited by the comfort of each individual. The number of original image frames exceeds 1500, which corresponds to the amount of data in many publicly available and commonly used datasets [3]. In particular, the collected dataset could be incorporated into other head configuration estimation algorithms. The experiments carried out on the overall system gave satisfactory results in terms of processing time, accuracy, and ease of use. Fast operation was achieved by dividing the problem into smaller parts (each solved by a different estimator) and optimal communication between the components. The time between head movement and the corresponding action of the surveillance camera was, on average, equal to 1.5 s. This includes head detection, orientation class estimation, sending control signals to CCTV, and actual movement. The processing time is not suitable for security applications, e.g., anomaly recognition systems. For this kind of security system, usually, the camera mounting needs to follow fast-moving objects, contrary to CCTV applications, where the camera is wide-angle and its movements are less dynamic. Therefore, the processing speed is sufficient for the problem considered. The accuracy of the head orientation estimators lies within the results achieved recently [3]. However, the accuracy tends to decrease for a larger number of output classes. In this work, the number of possible estimated head orientations is limited to 17, which is far from a continuous estimation. Nevertheless, the proposed discrete set of classes is sufficient for a given problem. Preliminary research has been done on more densely defined output classes. This solution was abandoned due to user dissatisfaction. Our main goal was to create a comfortable system and limit the fatigue of disabled users. Bear in mind that the system is intended to be used for many hours, and it is obvious that holding the head still could be exhausting for everyone. This was the main reason for choosing the hysteresis-based control action for the servomechanisms. The application of hysteresis ensures that slight movements of the head do not activate the servomechanism.