Temporal difference based adaptive object Detection (ToDo) platform at Edge Computing System
Yousung Yang, Kug Han, Seongsoo Lee, Joohyung Lee · 2020
This paper designs and implements a novel Temporal difference based adaptive object Detection (ToDo) platform in the video analytics at edge computing system. The proposed ToDo platform contains object tracking function and monitoring function, respectively. Based on temporal difference, the proposed ToDo skips the frames for object detection through adaptively controlling object detection rates. Then, it provides a monitoring function of object tracking and resource usages. The proposed ToDo platform is implemented on commercial edge node Jetson TX2 by utilizing YOLO (You Only Look Once) v3, and Dashboard, respectively. We evaluate its performance through extensive measurement-based analysis, and reveal that the proposed ToDo reduces GPU memory footprint to 17% while conducting moving objects detection with 7% accuracy loss.