# CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge

arXiv: https://arxiv.org/abs/2002.03797 · HTML: https://mlsanity.com/paper/2002.03797/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems](https://mlsanity.com/paper/2105.14195/) (2021)
- [Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective](https://mlsanity.com/paper/2211.02812/) (2022)
- [TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing](https://mlsanity.com/paper/2507.09448/) (2025)

## Abstract (arXiv)

Today, video cameras are deployed in dense for monitoring physical places e.g., city, industrial, or agricultural sites. In the current systems, each camera node sends its feed to a cloud server individually. However, this approach suffers from several hurdles including higher computation cost, large bandwidth requirement for analyzing the enormous data, and privacy concerns. In dense deployment, video nodes typically demonstrate a significant spatio-temporal correlation. To overcome these obstacles in current approaches, this paper introduces CONVINCE, a new approach to look at the network cameras as a collective entity that enables collaborative video analytics pipeline among cameras. CONVINCE aims at 1) reducing the computation cost and bandwidth requirements by leveraging spatio-temporal correlations among cameras in eliminating redundant frames intelligently, and ii) improving vision algorithms' accuracy by enabling collaborative knowledge sharing among relevant cameras. Our results demonstrate that CONVINCE achieves an object identification accuracy of ~91%, by transmitting only about ~25% of all the recorded frames.