CODS: Cloud-assisted Object Detection for Streaming Videos on Edge Devices

The advance of hardware has allowed edge devices to carry out varying applications, including computation-intensive machine learning tasks. This paper targets a real-time application of detecting objects on streaming videos with a tightened requirement on processing overhead. While the edge device still lacks the computation power to apply the object detection algorithm on every video frame, this paper integrates cloud-side servers in the solution as much prior work has attempted. However, our design is different from the conventional off-loading solutions that delegate the computation tasks to the cloud servers. We present a solution named CODS where the computation tasks are still conducted by the edge devices while the cloud server provides useful guidelines for choosing appropriate analysis algorithms. We implement our solution with the Jetson Nano device and evaluate it with videos from representative datasets. The results show that CODS is quite effective and superior to baseline alternatives.