EdgeCam: A Distributed Camera Operating System for Inference Scheduling and Continuous Learning

Deep Neural Networks (DNNs) are commonly used in camera systems for video surveillance. However, the computational demands of DNN inference pose challenges for on-edge video analytics due to potential delay. Additionally, edge cameras typically employ lightweight models, which are susceptible to data drift. In this demo, we present EdgeCam, an open-source distributed camera operating system that incorporates inference scheduling and continuous learning for video analytics. EdgeCam comprises multiple edge nodes and the cloud, enabling collaborative video analytics. Edge nodes also collect drift data to support continuous learning and maintain recognition accuracy. We have implemented essential functionalities and algorithms, ensuring modularity and ease of configuration. The source code of EdgeCam is at https://github.com/MSNLAB/EdgeCam.