###### Abstract  
Existing elevator systems lack the ability to display the number of people waiting on each floor and inside the elevator. This causes an inconvenience as users cannot tell if they should wait or seek alternatives, leading to unnecessary time wastage. In this work, we adopted edge computing by running the MobileNet-Single-Stage Object Detection (SSD) algorithm on edge devices to recognize the number of people inside an elevator and waiting on each floor. To ensure the accuracy of people counting, we fine-tuned the SSD parameters, such as the recognition frequency and confidence thresholds, and utilized the line of interest (LOI) counting strategy for people counting. In our experiment, we deployed four NVIDIA Jetson Nano boards in a four-floor building as edge devices to count people when they entered specific areas. The counting results, such as the number of people waiting on each floor and inside the elevator, were provided to users through a web app. Our experimental results demonstrate that the proposed method achieved an average accuracy of 85% for people counting. Furthermore, when comparing it to sending all images back to a remote server for people counting, the execution time required for edge computing was shorter, without compromising the accuracy significantly.  
indoor people counting; object tracking; image recognition; edge computing; Internet of Things +
Footnote †: journal: _Future Internet_  
the probabilistic model performed better than the convolutional neural network (CNN), especially when individuals were in a static state, with an accuracy of approximately 90%. However, in larger spaces, adopting this method may have a low accuracy due to sources of environmental interference, such as noise and weak signals. [PERSON] et al. (2018) employed radar for crowd tracking. They developed a background subtraction method to extract the positions of individuals. Similarly, [PERSON] et al. (2018) used various non-image methods for people flow detection, such as gas, radar, Wi-Fi, and Bluetooth. For these sensor-based methods, the major challenges and limitations are that the accuracy of people counting could be affected via environmental noise, signal strength, spatial layout, and so on.  
Compared to sensor-based methods, image-based approaches offer advantages for people counting, as they are not constrained via spatial limitations and can capture all spatial information within a frame. However, when utilizing only image-based models with the region of interest (ROI) and line of interest (LOI) methods, challenges may arise when dealing with stationary individuals or overlapping targets. The system might encounter difficulties with accurately distinguishing individuals when targets are stationary or overlapping with others, leading to a decline in the counting accuracy. In such scenarios, where targets exhibit little or no apparent motion or separation, the boundaries of the individuals in the image may become unclear, introducing disturbances and errors into the counting process. Furthermore, to date, there have been no studies combining the LOI and ROI methods for people counting. LOI is commonly used to detect whether targets pass through a virtual line, while ROI is employed to mark regions of interest. Combining these two methods could potentially enhance the accuracy of people flow counting, but there is currently a lack of corresponding research and methodologies to achieve this integration. As such, further exploration and the development of a combined LOI and ROI approach are warranted to address the challenges posed by stationary individuals and overlapping targets and to improve the precision and reliability of people counting in image-based methods.