Edge Cloud Ensemble with Motion Vectors for Object Detection in Wireless Environments

Cloud offloading enables smaller edge devices which contain fewer computing resources to be used for computer vision. This contributes to mobile applications of computer vision such as robotics and augmented reality. However, cloud offloading is impacted by bandwidth fluctuations in wireless networks. When the available bandwidth is restricted, it is difficult to offload workloads (i.e. frame) to cloud instances. Instead of offloading workloads to the cloud, existing approaches send a result of edge prediction or use a past cloud prediction to cover non-offloaded frames, which result in low accuracy. In this paper, we propose an Edge Cloud Ensemble method for object detection to improve accuracy in low bandwidth environments. In our method, the edge sends an inaccurate prediction and a motion vector of a detected object’s region to the cloud while maintaining low transmission overhead. The cloud corrects the inaccurate prediction by using the motion vectors to shift a past, accurate cloud prediction. The results of our experiments demonstrate that our approach can improve accuracy in low bandwidth environments compared with existing methods, especially in moving cameras.