# REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking

arXiv: https://arxiv.org/abs/1911.09217 · HTML: https://mlsanity.com/paper/1911.09217/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things](https://mlsanity.com/paper/2301.03561/) (2023)
- [Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety](https://mlsanity.com/paper/2302.04310/) (2023)
- [Edge-Based Video Analytics: A Survey](https://mlsanity.com/paper/2303.14329/) (2023)
- [Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety](https://mlsanity.com/paper/2212.12936/) (2022)
- [Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance](https://mlsanity.com/paper/2201.05739/) (2022)
- [A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications](https://mlsanity.com/paper/2107.06835/) (2021)
- [MoFM: A Large-Scale Human Motion Foundation Model](https://mlsanity.com/paper/2502.05432/) (2025)
- [ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation](https://mlsanity.com/paper/2202.00753/) (2022)
- [A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems](https://mlsanity.com/paper/2502.06581/) (2025)
- [Real-Time Privacy Preservation for Robot Visual Perception](https://mlsanity.com/paper/2505.05519/) (2025)
- [Single Run Action Detector over Video Stream -- A Privacy Preserving Approach](https://mlsanity.com/paper/2102.03391/) (2021)
- [C^3Net: End-to-End deep learning for efficient real-time visual active camera control](https://mlsanity.com/paper/2107.13233/) (2021)
- [Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification](https://mlsanity.com/paper/2306.03993/) (2023)

## Abstract (arXiv)

This article presents REVAMP2T, 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. 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, 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, REVAMP2T 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·Efficiency (AE), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP2T outperforms current state-of-the-art by as much as thirteen-fold AE~improvement.