Edge-assisted Online On-device Object Detection for Real-time Video Analytics

Real-time on-device object detection for video analytics fails to meet the accuracy requirement due to limited resources of mobile devices while offloading object detection inference to edges is time-consuming due to the transference of video data over edge networks. Based on the system with both on-device object tracking and edge-assisted analysis, we formulate a non-linear time-coupled program over time, maximizing the overall accuracy of object detection by deciding the frequency of edge-assisted inference, under the consideration of both dynamic edge networks and the constrained detection latency. We then design a learning-based online algorithm to adjust the threshold for triggering edge-assisted inference on the fly in terms of the object tracking results, which essentially controls the deviation of on-device tracking between two consecutive frames in the video, by only taking previously observable inputs. We rigorously prove that our approach only incurs sub-linear dynamic regret for the optimality objective. At last, we implement our proposed online schema, and extensive testbed results with real-world traces confirm the empirical superiority over alternative algorithms, in terms of up to 36% improvement on detection accuracy with ensured detection latency.