Paper Info
Title
Model Predictive Compression for Drone Video Analytics
Abstract
Drones will be increasingly deployed in surveillance scenarios, disaster zones, and remote areas. The videos collected from drone cameras provide site surveys, summaries, detect and track multiple targets. Today such videos are processed offline after the drone flight. Real- time processing provides several opportunities but has two key challenges - network bandwidth on the drone-to-server link is constrained and the computational capability of the drone processor is limited in terms of applying machine vision in real time. We propose a model predictive compression algorithm that uses predicted drone trajectory to select and transmit the most important image frames to the ground station to maximize the application utility while minimizing the network bandwidth use. The proposed compression scheme works in real-time on the drone processor because it estimates background motion without computing image features. To correct the model inaccuracies, the drone receives feedback from the ground station that can compute image features in real time. Evaluation results suggest that the proposed compression approach reduces network bandwidth overheads by 50-72% while ensuring high-quality mosaics in the drone mosaicing application.
Year
DOI
Venue
2018
10.1109/SECONW.2018.8396351
2018 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops)
Keywords
Field
DocType
drone mosaicing application,network bandwidth overheads,compression scheme,network bandwidth use,ground station,drone trajectory,model predictive compression algorithm,drone processor,computational capability,drone-to-server link,drone flight,drone cameras,remote areas,disaster zones,surveillance scenarios,drone video analytics
Machine vision,Feature (computer vision),Computer science,Server,Real-time computing,Bandwidth (signal processing),Drone,Data compression,Analytics,Trajectory
Conference
ISSN
ISBN
Citations 
2155-5494
978-1-5386-5242-8
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Aakanksha Chowdhery115512.28
Mung Chiang27303486.32