# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 6 Conclusions  
This study examined a room monitoring system using deep learning and perspective correction techniques. It also proposed a new person detection scheme that combines the Mask R-CNN scheme and the tile technique, including two algorithms to merge bounding boxes. For person detection, this study compared the proposed Mask R-CNN + Tiles scheme with the MobileNet-SSD, YOLO v3, and Mask R-CNN schemes. The results showed that the proposed scheme outperformed the MobileNet-SSD, YOLO v3, and Mask R-CNN schemes in terms of detection accuracy, MAE, and RMSE. Further, analyses and evaluations of the results indicated that our proposed system was practical and provided outstanding performance. Additionally, new person coordinates in the 2D image were generated from the final bounding boxes and a heat map was created based on the 2D image; these should enable users to examine the system quickly in different views. There are two assumptions of our system. Firstly, the video camera has to send an image to our machine (GPU-based computational environment) every 10 s. Basically, traditional video cameras do not provide ftp client service, therefore, we installed a virtual machine (using Oracle VM VirtualBox tool to run Windows 7) in our operating system (Windows 10) to open the full screen of IE browser and send the images of the browser to our back-end ftp server for analysis. In addition, we wrote an image capture program (Python code) for the virtual machine and that enabled images to be sent to our provided ftp server every 10 s for people counting service in our GPU-based machine. Secondly,  
Figure 17: Predicted number of people in areas. the study is run on a single machine and the computing time will be doubled if two or more than two images are simultaneously processed. Therefore, we set process schedule for the incoming images because one image can be processed by one GPU card at a time. There are many limitations when identifying people in spatial images and some improvements can be made in the future to increase the accuracy of identification. For example, two or more video cameras can be mounted in front, front left and front right of target people in a computer lab to avoid the situation where people are blocked by their computers. When outdoors, we can place video cameras on the path where people walk to collect images. It is also important to avoid occlusion, such as the occlusion of leaves. The less people are concealed, the better the image is. In addition, images captured by a single video camera can hardly provide enough people information for accurately calculating the amount of people. Therefore, it is suggested that at least two video cameras are used to perform target capture and increase the accuracy of identification through cross-comparison of the images. In the future, we will improve the processing time of this system by applying parallel processing techniques and continue to collect and analyze data for the system. We also intend to extend this system to other areas such as retail stores and supermarkets. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data and contributed to the materials and analysis tools; [PERSON] and [PERSON] wrote the manuscript. Finally, [PERSON], [PERSON], and [PERSON] gave relevant materials and valuable comments for this paper. All authors have read and agreed to the published version of the manuscript. The proposed system has been successfully put into practice to develop several projects, including people counting for reading and computer rooms in Feng Chia University. In the future, this system will be implemented to supervise people and generate real-time alerts when they fail to follow social distancing rules during a pandemic. This work was supported by the Ministry of Science and Technology, Republic of China (Grant Number: MOST107-2119-M-035-006).