# Edge-Based Video Analytics: A Survey

arXiv: https://arxiv.org/abs/2303.14329 · HTML: https://mlsanity.com/paper/2303.14329/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 156 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 30 of 30)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Learned Video Compression](https://mlsanity.com/paper/1811.06981/) (2018)
- [NoScope: Optimizing Neural Network Queries over Video at Scale](https://mlsanity.com/paper/1703.02529/) (2017)
- [Scaling Video Analytics on Constrained Edge Nodes](https://mlsanity.com/paper/1905.13536/) (2019)
- [A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics](https://mlsanity.com/paper/1703.02952/) (2017)
- [Fast Video Classification via Adaptive Cascading of Deep Models](https://mlsanity.com/paper/1611.06453/) (2016)
- [Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning](https://mlsanity.com/paper/1707.04991/) (2017)
- [REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking](https://mlsanity.com/paper/1911.09217/) (2019)
- [Scaling Video Analytics Systems to Large Camera Deployments](https://mlsanity.com/paper/1809.02318/) (2018)
- [Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase](https://mlsanity.com/paper/1812.02863/) (2018)
- [QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.02482/) (2018)
- [Approximate Query Service on Autonomous IoT Cameras](https://mlsanity.com/paper/1909.00841/) (2019)
- [QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey](https://mlsanity.com/paper/1912.12467/) (2019)
- [Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices](https://mlsanity.com/paper/1709.09503/) (2017)
- [Privacy-Preserving Deep Inference for Rich User Data on The Cloud](https://mlsanity.com/paper/1710.01727/) (2017)
- [Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT](https://mlsanity.com/paper/2103.06472/) (2021)
- [Fast Integral Histogram Computations on GPU for Real-Time Video Analytics](https://mlsanity.com/paper/1711.01919/) (2017)
- [Harnessing constrained resources in service industry via video analytics](https://mlsanity.com/paper/1807.00139/) (2018)
- [Large Scale Audio-Visual Video Analytics Platform for Forensic Investigations of Terroristic Attacks](https://mlsanity.com/paper/1811.11623/) (2018)
- [VideoDP: A Universal Platform for Video Analytics with Differential Privacy](https://mlsanity.com/paper/1909.08729/) (2019)

## Built on by (most cited first, 8 of 8)

- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges](https://mlsanity.com/paper/2507.02074/) (2025)
- [Deep Video Codec Control for Vision Models](https://mlsanity.com/paper/2308.16215/) (2023)
- [A Perspective on Deep Vision Performance with Standard Image and Video Codecs](https://mlsanity.com/paper/2404.12330/) (2024)
- [Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision](https://mlsanity.com/paper/2603.13306/) (2026)
- [FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution](https://mlsanity.com/paper/2503.13745/) (2025)
- [CroBIM-V: Memory-Quality Controlled Remote Sensing Referring Video Object Segmentation](https://mlsanity.com/paper/2601.12076/) (2026)

## Abstract (arXiv)

Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge computing in essence aims to overcome this hindrance by processing most video data making use of edge servers, such as small-scale on-premises server clusters, server-grade computing resources at mobile base stations and even mobile devices like smartphones and tablets; hence, the term edge-based video analytics. However, the actual realization of such analytics requires more than the simple, collective use of edge servers. In this paper, we survey state-of-the-art works on edge-based video analytics with respect to applications, architectures, techniques, resource management, security and privacy. We provide a comprehensive and detailed review on what works, what doesn't work and why. These findings give insights and suggestions for next generation edge-based video analytics. We also identify open issues and research directions.