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Abstract
In the domain of railway infrastructure, monitoring
and securing the operational stability of the operation remains a
significant problem. Vandalism, trespassing, sabotage and theft
are constant threats, endangering the safety and integrity of the
entire system. At the same time monitoring of these systems
is becoming harder and harder as the systems grow and the
amount of data produced by the surveillance equipment scales accordingly. Additionally, since specific sensor modalities can have weaknesses in detecting one kind of threat, it is often necessary to install different sensors to get a better understanding of situation. In this paper we present our fusion model based on
Probabilistic Occupancy Maps (POM) and Bayesian Inference for environmental mapping of critical events such as vandalism and trespassing in the vicinity of railway infrastructure. We show that this approach helps to increase accuracy, while simultaneously decreasing the amount of false alarms generated by a system.
and securing the operational stability of the operation remains a
significant problem. Vandalism, trespassing, sabotage and theft
are constant threats, endangering the safety and integrity of the
entire system. At the same time monitoring of these systems
is becoming harder and harder as the systems grow and the
amount of data produced by the surveillance equipment scales accordingly. Additionally, since specific sensor modalities can have weaknesses in detecting one kind of threat, it is often necessary to install different sensors to get a better understanding of situation. In this paper we present our fusion model based on
Probabilistic Occupancy Maps (POM) and Bayesian Inference for environmental mapping of critical events such as vandalism and trespassing in the vicinity of railway infrastructure. We show that this approach helps to increase accuracy, while simultaneously decreasing the amount of false alarms generated by a system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of ISIF International Conference on Information Fusion (FUSION 2024) |
| Pages | 1-7 |
| Volume | 27 |
| ISBN (Electronic) | 978-1-7377497-6-9 |
| DOIs | |
| Publication status | Published - 15 Oct 2024 |
| Event | 27th International Conference on Information Fusion - San Giobbe Economics Campus Fondamenta San Giobbe, Cannaregio 873, Venice, Italy Duration: 8 Jul 2024 → 11 Jul 2024 https://fusion2024.org/ |
Conference
| Conference | 27th International Conference on Information Fusion |
|---|---|
| Abbreviated title | FUSION 2024 |
| Country/Territory | Italy |
| City | Venice |
| Period | 8/07/24 → 11/07/24 |
| Internet address |
Research Field
- Responsive Sensing & Analytics
- Computer Vision
Keywords
- Bayesian inference
- Multi-modal information fusion
- critical infrastructure protection
- trust in fusion systems
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Dive into the research topics of 'A Bayesian Approach - Data Fusion for robust detection of Vandalism and Trespassing related events in the context of railway security'. Together they form a unique fingerprint.Research output
- 2 Article
-
A Multimodal Fusion Architecture for Sensor Applications
Hubner, M. & Nausner, J., 15 Jan 2025, In: ERCIM 140. 140, p. 28-29 2 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Robust Detection of Critical Events in the Context of Railway Security based on Multimodal Sensor Data Fusion
Hubner, M., Wohlleben, K., Litzenberger, M., Veigl, S., Opitz, A., Grebien, S., Graf, F., Haderer, A., Rechbauer, S. & Poltschak, S., 25 Jun 2024, In: Sensors. 24, 13, 18 p., 4118.Research output: Contribution to journal › Article › peer-review
Open Access