### Our Contributions  
We propose an indoor elevator people flow counting system based on an image analysis. The system leverages image data to perform real-time tracking of the number of individuals inside the elevator and those waiting for the elevator in the building. The acquired data are subsequently transmitted to a central server and presented on a large display screen, providing an overview of the people flow information to all occupants of the building. The system features a mobile interface that allows users to interact with the system by scanning a QR (quick response) code, granting access to real-time updates on the current elevator waiting status and the count of occupants inside the elevator. The displayed numerical values are intelligently color-coded (green, yellow, and red) to denote varying crowd density levels, thereby assisting users in making informed decisions regarding their elevator usage. Edge computing has emerged as a promising solution to the limitations of traditional cloud computing. [PERSON] et al. (2018) emphasized its benefits, including its reduced bandwidth usage, faster response times, enhanced security, and improved privacy protection. [PERSON] et al. (2018) demonstrated edge computing's practical advantages through an annotated visual dataset for real-time tomato detection, highlighting its potential to enhance automation and precision. Therefore, in the application of elevator waiting people counting, edge computing can minimize the latency when counting individuals waiting for elevators. Furthermore, it conserves the streaming bandwidth by transmitting only essential information, which not only reduces costs but also optimizes the efficiency of data communication within our system. In this work, we utilize MobileNet V3 for image recognition and single-stage object detection (SSD). MobileNet V3 combines depthwise separable convolutions from V1 and linear bottlenecks from V2, enhancing the accuracy with the Hard Swish activation function. The COCO dataset is used for \"person\" object recognition. SSD offers faster processing than the Faster RCNN by applying YOLO's concept for classification and bounding box regression. MobileNet V3 generates candidate regions for SSD detection. The centroid tracker algorithm tracks targets by predicting their positions. Counting is based on target centroids crossing the LOI. This method reduces the counting errors during occlusion and ensures efficient counting when targets enter the waiting area. In order to verify the accuracy of the system's counting method, we designed various scenarios with different elevator queue formations and positions of individuals inside the elevator, including configurations with three, five, and eight people. The elevator queue formations consisted of horizontal, vertical, diagonal, and criss-cross patterns, while the positions inside the elevator included non-overlapping and overlapping arrangements. Additionally, we considered scenarios involving pedestrian movements in corridors, such as walking in the same direction and encountering pedestrian crossings. The cameras were positioned on the walls near the elevator doors at a 45-degree angle to capture the waiting crowd and the individuals inside the elevator simultaneously. The test subjects' heights ranged from 170 to 180 cm, and the counting of individuals waiting for the elevator and inside the elevator was performed using the LOI approach. Furthermore, we conducted tests on both the Nvidia Jetson Nano Developer Kit embedded development board and a computer host using the same counting system. Regardless of whether it was run on the host computer or the embedded development board, our counting method achieved an average accuracy of 85% when counting the people flow. The rest of this paper is organized as follows: Section 2 explores the relevant literature on people-counting methods using the ROI and LOI. Section 3 explains the design requirements and challenges. Section 4 elaborates on the people-counting method and the system interface used in this system.