Traditional computer vision systems perform real-time object detection by using abundant computational resources in the cloud. Our project explores running these detection models on edge devices such as the Raspberry Pi to perform human detection in a live video stream. Implementing this detection system on an edge device requires design optimizations to overcome the lack of network connectivity and perform object detection in a way that is computationally feasible at the edge without cloud resources.