# SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning

arXiv: https://arxiv.org/abs/2001.01043 · HTML: https://mlsanity.com/paper/2001.01043/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Post-hoc Models for Performance Estimation of Machine Learning Inference](https://mlsanity.com/paper/2110.02459/) (2021)
- [A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices](https://mlsanity.com/paper/2505.16563/) (2025)
- [CANS: Communication Limited Camera Network Self-Configuration for Intelligent Industrial Surveillance](https://mlsanity.com/paper/2109.05665/) (2021)
- [ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence](https://mlsanity.com/paper/2203.13061/) (2022)
- [A Comprehensive Study on Artificial Intelligence Algorithms to Implement Safety Using Communication Technologies](https://mlsanity.com/paper/2205.08404/) (2022)
- [R2E-VID: Two-Stage Robust Routing via Temporal Gating for Elastic Edge-Cloud Video Inference](https://mlsanity.com/paper/2604.09681/) (2026)
- [TinyFormer: Preserving Tiny Objects in YOLO-DETR Hybrid Real-time Detectors](https://mlsanity.com/paper/2605.25046/) (2026)

## Abstract (arXiv)

The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype with multiple edge devices and a public Cloud, and conduct extensive experiments using realworld surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7x less bandwidth cost and 5.4x faster query response time than the cloud-only solution; and can improve query accuracy by up to 43.9% and achieve 15.8x speedup respectively, in comparison with edge-only approaches.