Automatic Passenger Counting (APC) systems are some of the many Internet-Of-Things (IoT) applications and have been increasingly adopted by public transportation companies in recent years. APCs provide valuable data that can be used to give an real time passenger count, which can be a convenient service and allow customers to plan their travels accordingly. The provided data is also valuable for resource streamlining and planning, which potentially increases revenues for the public transportation companies. This thesis briefly studies and evaluates different APC technologies, highlights the advantages and disadvantages of these, and presents an Edge-prototype based on Computer Vision and Object Detection. The presented APC was tested in a lab environment and with recordings of people walking in and out of a designated area in the lab. Test results from the lab environment show that the presented low-cost APC efficiently detects passengers with an accuracy of 98.6% on pre-recorded videos. The APC was also tested in real time and the results show that the low-cost APC only achieved an accuracy of 66.7%. This work has laid the ground for further development and testing in a public transport environment.