Dynamic Offloading on a Hybrid Edge–Cloud Architecture for Multiobject Tracking

Abstract

Multiobject tracking (MOT) using computer vision on smart cameras has become more popular owing to its continuously improving performance. However, the number of tracking targets may suddenly increase, which deteriorates the accuracy and robustness of MOT on existing edge cameras (smart cameras leveraging edge intelligence) because of their restricted computing and storage capacities. To address this issue, we propose dynamic computation offloading on a hybrid edge–cloud architecture. When the confidence of MOT decreases because of a sudden increase in tracking targets, an edge camera can dynamically offload its MOT tasks to backend servers and retake the tasks once confidence is recovered. The experiment results show that the accuracy of the hybrid system can be increased by up to 8% while reducing the network traffic. Therefore, the proposed hybrid approaches can maintain not only the quality and efficiency of image processing on edge cameras but also good tracking performance while reducing communication costs in an environment with dynamically changing people flow.