# Scaling Video Analytics on Constrained Edge Nodes

arXiv: https://arxiv.org/abs/1905.13536 · HTML: https://mlsanity.com/paper/1905.13536/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 30 of 36)

- [GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge](https://mlsanity.com/paper/2201.07705/) (2022)
- [Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning](https://mlsanity.com/paper/2205.14833/) (2022)
- [Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving](https://mlsanity.com/paper/2312.05385/) (2023)
- [Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems](https://mlsanity.com/paper/2209.05948/) (2022)
- [Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics](https://mlsanity.com/paper/2106.15315/) (2021)
- [ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild](https://mlsanity.com/paper/2512.06721/) (2025)
- [A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning](https://mlsanity.com/paper/2102.03012/) (2021)
- [CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale](https://mlsanity.com/paper/2105.06524/) (2021)
- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Edge-Based Video Analytics: A Survey](https://mlsanity.com/paper/2303.14329/) (2023)
- [AccMPEG: Optimizing Video Encoding for Video Analytics](https://mlsanity.com/paper/2204.12534/) (2022)
- [On Efficient Approximate Queries over Machine Learning Models](https://mlsanity.com/paper/2206.02845/) (2022)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations](https://mlsanity.com/paper/2304.02101/) (2023)
- [AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems](https://mlsanity.com/paper/2101.09752/) (2021)
- [Boosting DNN Cold Inference on Edge Devices](https://mlsanity.com/paper/2206.07446/) (2022)
- [Anatomizing Deep Learning Inference in Web Browsers](https://mlsanity.com/paper/2402.05981/) (2024)
- [Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning](https://mlsanity.com/paper/2107.03964/) (2021)
- [FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics](https://mlsanity.com/paper/2203.11493/) (2022)
- [AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics](https://mlsanity.com/paper/2407.20124/) (2024)
- [Why is the video analytics accuracy fluctuating, and what can we do about it?](https://mlsanity.com/paper/2208.12644/) (2022)
- [Towards Automatic Model Specialization for Edge Video Analytics](https://mlsanity.com/paper/2104.06826/) (2021)
- [OneAdapt: Fast Configuration Adaptation for Video Analytics Applications via Backpropagation](https://mlsanity.com/paper/2310.02422/) (2023)
- [Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions](https://mlsanity.com/paper/2504.15300/) (2025)
- [EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference](https://mlsanity.com/paper/2106.13842/) (2021)
- [BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge](https://mlsanity.com/paper/2312.15740/) (2023)
- [Budget-Adaptive Routing: Skipping the Weak When the Strong Answers Anyway](https://mlsanity.com/paper/2606.30919/) (2026)
- [VID-WIN: Fast Video Event Matching with Query-Aware Windowing at the Edge for the Internet of Multimedia Things](https://mlsanity.com/paper/2105.02957/) (2021)
- [Large-Scale Video Analytics through Object-Level Consolidation](https://mlsanity.com/paper/2111.15451/) (2021)
- [InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference](https://mlsanity.com/paper/2209.13873/) (2022)

## Abstract (arXiv)

As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestrian tracking to offload high-quality video streams to a datacenter. This paper presents FilterForward, a new edge-to-cloud system that enables datacenter-based applications to process content from thousands of cameras by installing lightweight edge filters that backhaul only relevant video frames. FilterForward introduces fast and expressive per-application microclassifiers that share computation to simultaneously detect dozens of events on computationally constrained edge nodes. Only matching events are transmitted to the cloud. Evaluation on two real-world camera feed datasets shows that FilterForward reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy for challenging video content.