# Scaling Video Analytics Systems to Large Camera Deployments

arXiv: https://arxiv.org/abs/1809.02318 · HTML: https://mlsanity.com/paper/1809.02318/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

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

- [Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures](https://mlsanity.com/paper/2205.04713/) (2022)
- [CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale](https://mlsanity.com/paper/2105.06524/) (2021)
- [Edge-Based Video Analytics: A Survey](https://mlsanity.com/paper/2303.14329/) (2023)
- [A novel efficient Multi-view traffic-related object detection framework](https://mlsanity.com/paper/2302.11810/) (2023)
- [Multi-Camera Trajectory Forecasting with Trajectory Tensors](https://mlsanity.com/paper/2108.04694/) (2021)
- [ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration](https://mlsanity.com/paper/2308.10068/) (2023)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [Responsive DNN Adaptation for Video Analytics against Environment Shift via Hierarchical Mobile-Cloud Collaborations](https://mlsanity.com/paper/2505.00745/) (2025)
- [RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics](https://mlsanity.com/paper/2512.24386/) (2025)

## Abstract (arXiv)

Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale video analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera video analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling video analytics to large camera networks, and outline a plan for its realization.