Authors: Giacomo D’Amicantonio, Egor Bondarau, Peter H.N. de With

Published on: April 19, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2404.12712

Summary

• What is new: uTRAND introduces a method to shift anomaly detection from pixel space to a semantic-topological domain, facilitating explainability and reducing the need for labeled data.
• Why this is important: Deep learning approaches are not always effective in real-world anomaly detection due to the lack of labeled data and the challenge of explaining predictions.
• What the research proposes: The uTRAND framework detects and tracks traffic agents in videos, translating the intersection into a graph to learn normal behavior and classify anomalies with simple, interpretable rules.
• Results: uTRAND surpasses existing techniques in detecting anomalous trajectories in real-world settings and offers explainable outcomes.

Technical Details

Technological frameworks used: uTRAND

Models used: nan

Data used: Bird’s-eye-view videos from traffic cameras

Potential Impact

Traffic monitoring, urban planning technologies, autonomous vehicle development companies, and surveillance system providers.

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