  In this work, we present DCC(Deeper-yet-Compatible Compression), one enabling
technique for real-time drone-sourced edge-assisted video analytics built on
top of the existing codec. DCC tackles an important technical problem to
compress streamed video from the drone to the edge without scarifying accuracy
and timeliness of video analytical tasks performed at the edge. DCC is inspired
by the fact that not every bit in streamed video is equally valuable to video
analytics, which opens new compression room over the conventional
analytics-oblivious video codec technology. We exploit drone-specific context
and intermediate hints from object detection to pursue adaptive fidelity needed
to retain analytical quality. We have prototyped DCC in one showcase
application of vehicle detection and validated its efficiency in representative
scenarios. DCC has reduced transmission volume by 9.5-fold over the baseline
approach and 19-683% over the state-of-the-art with comparable detection
accuracy.

Networking and Internet Architecture;Computer Vision and Pattern Recognition