# Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features

arXiv: https://arxiv.org/abs/2201.06804 · HTML: https://mlsanity.com/paper/2201.06804/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2022.

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

- [Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression](https://mlsanity.com/paper/2505.07119/) (2025)
- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)
- [Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/2605.14501/) (2026)

## Abstract (arXiv)

Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scaling with the number of cameras or with the observed scene complexity. With this context in mind, this paper introduces for the first time the idea of Stimulation Model (SM), as a mathematical relation between the set of detectable events and the corresponding stimulated cameras observing those events. The formulation of the related SM identification problem is proposed, along with a proper network observations model, and a solution approach based on deep embedded features and soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to provide a suitable description for data distribution, while an autoencoder is used to reduce undesired effects due to the so-called curse of dimensionality emerging in case of large scale networks. Then, it is shown that a SM can be learnt by solving Maximum A-Posteriori estimation on the encoded features belonging to a space with lower dimensionality. Numerical results on synthetic scenarios are reported to validate the devised estimation algorithm.