REVAMP${}^2$T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking

Abstract

This article presents REVAMP${}^2$T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacy-built-in decentralized situational awareness. REVAMP${}^2$T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP${}^2$T proposes a unified integrated computer vision pipeline for detection, re-identification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition, and tracks and re-identifies pedestrians based on their key features at runtime. On the IoT system side, REVAMP${}^2$T provides infrastructure to maximize hardware utilization on the edge, orchestrates global communications, and provides system-wide re-identification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, Accuracy$\cdot$Efficiency (Æ), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP${}^2$T outperforms current state-of-the-art by as much as thirteen-fold Æ~improvement.

Published as an article paper in IEEE Internet of Things Journal: Special Issue on Privacy and Security in Distributed Edge Computing and Evolving IoT