Video Feature Compression for Machine Tasks
Kiran Misra, Tianying Ji, Andrew Segall, Frank Bossen
Abstract
We consider the problem of transmitting video from a remote device to a cloud-based classification system in a bandwidth limited network. Our focus is on developing an end-to-end system that extracts features from the video data and compresses these features for transmission. In this paper, we consider approaches that operate on each video frame independently as well as exploiting the temporal correlation between frames. In both cases, the transmitted features can be used for object detection and instance segmentation tasks using existing, pre-trained networks. Results show the efficacy of the approach with improvements in coding efficiency ranging from 46.3% to 92.8% when compared to compressing the video data using state-of-the-art video compression standards.