  Recent advances in computer vision has led to a growth of interest in
deploying visual analytics model on mobile devices. However, most mobile
devices have limited computing power, which prohibits them from running large
scale visual analytics neural networks. An emerging approach to solve this
problem is to offload the computation of these neural networks to computing
resources at an edge server. Efficient computation offloading requires
optimizing the trade-off between multiple objectives including compressed data
rate, analytics performance, and computation speed. In this work, we consider a
"split computation" system to offload a part of the computation of the YOLO
object detection model. We propose a learnable feature compression approach to
compress the intermediate YOLO features with light-weight computation. We train
the feature compression and decompression module together with the YOLO model
to optimize the object detection accuracy under a rate constraint. Compared to
baseline methods that apply either standard image compression or learned image
compression at the mobile and perform image decompression and YOLO at the edge,
the proposed system achieves higher detection accuracy at the low to medium
rate range. Furthermore, the proposed system requires substantially lower
computation time on the mobile device with CPU only.
