An End-to-End Integrated Computation and Communication Architecture for Goal-oriented Networking: A Perspective on Live Surveillance Video

TL;DR

This paper introduces a source-based, situation-aware streaming architecture for live surveillance video that reduces power consumption while maintaining high classification accuracy for important events.

Contribution

It presents a novel integrated computation and communication framework that performs initial event detection at the source with a lightweight neural network, optimizing resource use.

Findings

Reduces transmitter power consumption by 38.5% for UHD video

Achieves 97.5% classification accuracy for important situations

Efficiently balances source processing and deep analysis for real-time surveillance

Abstract

Real-time video surveillance has become a crucial technology for smart cities, made possible through the large-scale deployment of mobile and fixed video cameras. In this paper, we propose situation-aware streaming, for real-time identification of important events from live-feeds at the source rather than a cloud based analysis. For this, we first identify the frames containing a specific situation and assign them a high scale-of-importance (SI). The identification is made at the source using a tiny neural network (having a small number of hidden layers), which incurs a small computational resource, albeit at the cost of accuracy. The frames with a high SI value are then streamed with a certain required Signal-to-Noise-Ratio (SNR) to retain the frame quality, while the remaining ones are transmitted with a small SNR. The received frames are then analyzed using a deep neural network (with many hidden layers) to extract the situation accurately. We show that the proposed scheme is able to reduce the required power consumption of the transmitter by 38.5% for 2160p (UHD) video, while achieving a classification accuracy of 97.5%, for the given situation.