# CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale

arXiv: https://arxiv.org/abs/2105.06524 · HTML: https://mlsanity.com/paper/2105.06524/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Features for Multi-Target Multi-Camera Tracking and Re-Identification](https://mlsanity.com/paper/1803.10859/) (2018)
- [SBNet: Sparse Blocks Network for Fast Inference](https://mlsanity.com/paper/1801.02108/) (2018)
- [Focus: Querying Large Video Datasets with Low Latency and Low Cost](https://mlsanity.com/paper/1801.03493/) (2018)
- [Scaling Video Analytics on Constrained Edge Nodes](https://mlsanity.com/paper/1905.13536/) (2019)
- [Scaling Video Analytics Systems to Large Camera Deployments](https://mlsanity.com/paper/1809.02318/) (2018)
- [Pano: Optimizing 360deg Video Streaming with a Better Understanding of Quality Perception](https://mlsanity.com/paper/1911.04139/) (2019)

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

- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [A novel efficient Multi-view traffic-related object detection framework](https://mlsanity.com/paper/2302.11810/) (2023)
- [AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics](https://mlsanity.com/paper/2407.20124/) (2024)
- [ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration](https://mlsanity.com/paper/2308.10068/) (2023)
- [Learning Online Policies for Person Tracking in Multi-View Environments](https://mlsanity.com/paper/2312.15858/) (2023)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications](https://mlsanity.com/paper/2501.10396/) (2024)
- [TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing](https://mlsanity.com/paper/2507.09448/) (2025)
- [OmniSense: Towards Edge-Assisted Online Analytics for 360-Degree Videos](https://mlsanity.com/paper/2508.14237/) (2025)

## Abstract (arXiv)

Video cameras are pervasively deployed in city scale for public good or community safety (i.e. traffic monitoring or suspected person tracking). However, analyzing large scale video feeds in real time is data intensive and poses severe challenges to network and computation systems today. We present CrossRoI, a resource-efficient system that enables real time video analytics at scale via harnessing the videos content associations and redundancy across a fleet of cameras. CrossRoI exploits the intrinsic physical correlations of cross-camera viewing fields to drastically reduce the communication and computation costs. CrossRoI removes the repentant appearances of same objects in multiple cameras without harming comprehensive coverage of the scene. CrossRoI operates in two phases - an offline phase to establish cross-camera correlations, and an efficient online phase for real time video inference. Experiments on real-world video feeds show that CrossRoI achieves 42% - 65% reduction for network overhead and 25% - 34% reduction for response delay in real time video analytics applications with more than 99% query accuracy, when compared to baseline methods. If integrated with SotA frame filtering systems, the performance gains of CrossRoI reach 50% - 80% (network overhead) and 33% - 61% (end-to-end delay).