# Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing

arXiv: https://arxiv.org/abs/2004.10977 · HTML: https://mlsanity.com/paper/2004.10977/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

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

- [A VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories](https://mlsanity.com/paper/2506.05405/) (2025)
- [Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System](https://mlsanity.com/paper/2302.12004/) (2023)
- [Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data](https://mlsanity.com/paper/2309.03439/) (2023)
- [Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing](https://mlsanity.com/paper/2312.02491/) (2023)

## Abstract (arXiv)

The use of video-imaging data for in-line process monitoring applications has become more and more popular in the industry. In this framework, spatio-temporal statistical process monitoring methods are needed to capture the relevant information content and signal possible out-of-control states. Video-imaging data are characterized by a spatio-temporal variability structure that depends on the underlying phenomenon, and typical out-of-control patterns are related to the events that are localized both in time and space. In this paper, we propose an integrated spatio-temporal decomposition and regression approach for anomaly detection in video-imaging data. Out-of-control events are typically sparse spatially clustered and temporally consistent. Therefore, the goal is to not only detect the anomaly as quickly as possible ("when") but also locate it ("where"). The proposed approach works by decomposing the original spatio-temporal data into random natural events, sparse spatially clustered and temporally consistent anomalous events, and random noise. Recursive estimation procedures for spatio-temporal regression are presented to enable the real-time implementation of the proposed methodology. Finally, a likelihood ratio test procedure is proposed to detect when and where the hotspot happens. The proposed approach was applied to the analysis of video-imaging data to detect and locate local over-heating phenomena ("hotspots") during the layer-wise process in a metal additive manufacturing process.