  Edge devices, such as cameras and mobile units, are increasingly capable of
performing sophisticated computation in addition to their traditional roles in
sensing and communicating signals. The focus of this paper is on collaborative
object detection, where deep features computed on the edge device from input
images are transmitted to the cloud for further processing. We consider the
impact of packet loss on the transmitted features and examine several ways for
recovering the missing data. In particular, through theory and experiments, we
show that methods for image inpainting based on partial differential equations
work well for the recovery of missing features in the latent space. The
obtained results represent the new state of the art for missing data recovery
in collaborative object detection.

Computer Vision and Pattern Recognition;Multimedia