In recent years, significant progress has been made in computer vision regarding object detection and tracking which has allowed the emergence of various applications. These often focus on identifying and tracking people in different environments such as buildings.
Detecting people allows us to get a more comprehensive view of people flow as traditional IoT data from elevators cannot track individual people and their trajectories. In this thesis, we concentrate on people detection in elevator lobbies which we can use to improve the efficiency of the elevators and the convenience of the building. We compare the performance and speed of various object detection algorithms. Additionally, we research an edge device's capability to run an object detection model on multiple cameras and whether a single device can cover the target building.
We were able to train an object detection algorithm suitable for our application. This allowed accurate people detection that can be used for people counting. We found that out of the three object detection algorithms we trained, YOLOv3 was the only one capable of generalizing to unseen environments, which is essential for general purpose application. The performances of the other two models (SSD and Faster R-CNN) were poor in terms of either accuracy or speed. Based on these, we chose to deploy YOLOv3 to the edge device. We found that the edge device's inference time is linearly dependent on the number of cameras. Therefore, we can conclude that one edge device should be sufficient for our target building, allowing two cameras for each floor. We also demonstrated that the edge device allows easy addition of an object tracking layer, which is required for the solution to work in a real-life office building
