# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
## 4 Conclusions  
In addition to the previous chapter where the results were presented, some other ideas and relations to the research of others may be discussed here. This study shows how the hardware that is already deployed may be used for another purpose in the case of smart cities and buildings, when only a software module with a control unit is added. The only prerequisite is having access to real-time image data of the installed camera system and computing capabilities for image processing, which can be run locally or in the cloud. In general, the price of a smart building is already high, and it increases with every added technology. This approach may lead to lower cost and create a Shared Sensory System for all AGVs at the level of a whole building and surrounding area. We presented a possible use case when images of face-recognition security cameras, which were placed above the doors to provide automatic unlocking and opening, were also used for tracking of robots and other devices. This method can be applied to a robot navigation adding algorithm [38] for human and object detection of autonomous cars. Such capabilities would allow to stop or slow down a robot when a person is approaching while hidden behind a corner, so the human is not in the robot's field of view. Alternatively, the methodology may be used along with SLAM to add another safety feature to the control system. The choice of camera resolution has an influence on the reliability and processing time of the system. In our measurements, for the real-time tracking application, the 640 \(\times\) 480 px resolution would be necessary, but the drop in the case of accuracy might be a problem. However, the processing  
Figure 11: Error between detected and real position of the robots: (**a**) \(X\) direction of white robot; (**b**) \(X\) direction of black robot; (**c**) \(Y\) direction of white robot; (**d**) \(Y\) direction of black robot. Figure 12: Calculated velocity of robots: (**a**) white robot; (**b**) black robot. time may be solved if more powerful control PC is used. In the experiment, we used resolution \(1280\times 720\) px, which provided the best ratio between accuracy and speed of localization for us. When more cameras are deployed, a higher computing power is demanded. Therefore, more processing units or cloud computing for analyzing the images should be considered. Another disadvantage of the system is that a device position might be lost if it is not detected by any camera. In this case, the control system should be prepared for dealing with such a situation. For example, the robot shall be stopped until allowed to continue in its trajectory or reset. For the most accurate detection of a tag, every camera should be calibrated. On the other hand, if many cameras of the same type are deployed, the calibration constants can be determined statistically performing the calibration with a few of these cameras and distributing the calibration file with constants of average values. If a new camera is added to the system, its precise position can be determined using an additional device with tag markers placed at a certain distance from the camera using the device's known dimensions. In multiple camera detection, we use a simple distance average for the detected objects. Another approach to determine the position more accurately is to apprise the distance of every camera to the detected object. Therefore, the nearest camera that should provide the most precise detection would obtain a higher weight than the others. This is a topic for further research. Our algorithm can deal with multiple marker detection. This can be deployed whenever in a building with coverage of the camera's field of view and used for localization of any device with the marker, not only robots. In addition to that, this system could be applied in the outdoor environment using the surveillance camera systems deployed by governments.