REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking | IEEE Journals & Magazine | IEEE Xplore

REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking


Abstract:

This article presents real-time edge video analytics for multicamera privacy-aware pedestrian tracking (REVAMP2T), as an integrated end-to-end Internet of Things (IoT) sy...Show More

Abstract:

This article presents real-time edge video analytics for multicamera privacy-aware pedestrian tracking (REVAMP2T), as an integrated end-to-end Internet of Things (IoT) system for privacy built-in decentralized situational awareness. REVAMP2T 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, REVAMP2T proposes a unified integrated computer vision pipeline for detection, reidentification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition and tracks and reidentifies the pedestrians based on their key features at runtime. On the IoT system side, REVAMP2T provides an infrastructure to maximize the hardware utilization on the edge, orchestrates global communications, and provides system-wide reidentification, 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-efficiency (Æ), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP2T outperforms the current state of the art by as much as 13-fold Æ improvement.
Published in: IEEE Internet of Things Journal ( Volume: 7, Issue: 4, April 2020)
Page(s): 2591 - 2602
Date of Publication: 20 November 2019

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