# ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale

arXiv: https://arxiv.org/abs/1811.01268 · HTML: https://mlsanity.com/paper/1811.01268/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision](https://mlsanity.com/paper/2112.09852/) (2021)
- [Towards Live Video Analytics with On-Drone Deeper-yet-Compatible Compression](https://mlsanity.com/paper/2111.06263/) (2021)
- [CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios](https://mlsanity.com/paper/2410.18399/) (2024)
- [DMVC: Multi-Camera Video Compression Network aimed at Improving Deep Learning Accuracy](https://mlsanity.com/paper/2410.18400/) (2024)

## Abstract (arXiv)

Enterprises are increasingly deploying large camera networks for video analytics. Many target applications entail a common problem template: searching for and tracking an object or activity of interest (e.g. a speeding vehicle, a break-in) through a large camera network in live video. Such cross-camera analytics is compute and data intensive, with cost growing with the number of cameras and time. To address this cost challenge, we present ReXCam, a new system for efficient cross-camera video analytics. ReXCam exploits spatial and temporal locality in the dynamics of real camera networks to guide its inference-time search for a query identity. In an offline profiling phase, ReXCam builds a cross-camera correlation model that encodes the locality observed in historical traffic patterns. At inference time, ReXCam applies this model to filter frames that are not spatially and temporally correlated with the query identity's current position. In the cases of occasional missed detections, ReXCam performs a fast-replay search on recently filtered video frames, enabling gracefully recovery. Together, these techniques allow ReXCam to reduce compute workload by 8.3x on an 8-camera dataset, and by 23x - 38x on a simulated 130-camera dataset. ReXCam has been implemented and deployed on a testbed of 5 AWS DeepLens cameras.