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[2602.16057] Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
Summary
This article presents a novel multi-view tensor decomposition framework to analyze rail crossing behaviors from video data, revealing significant insights into driver behavior patterns across different locations and conditions.
Why It Matters
Understanding rail crossing behavior is crucial for enhancing safety measures. This research provides a scalable method to identify behavioral patterns, which can inform targeted interventions to reduce accidents at railway crossings, ultimately improving public safety.
Key Takeaways
- Introduces a multi-view tensor decomposition framework for analyzing rail crossing behaviors.
- Identifies location as a stronger determinant of behavior patterns than time of day.
- Reveals distinct behavioral clusters based on approach-phase behaviors.
- Utilizes TimeSformer embeddings for effective representation of video phases.
- Provides a foundation for scalable safety interventions at railway crossings.
Computer Science > Machine Learning arXiv:2602.16057 (cs) [Submitted on 17 Feb 2026] Title:Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods Authors:Dawon Ahn, Het Patel, Aemal Khattak, Jia Chen, Evangelos E. Papalexakis View a PDF of the paper titled Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods, by Dawon Ahn and 4 other authors View PDF HTML (experimental) Abstract:Railway crossings present complex safety challenges where driver behavior varies by location, time, and conditions. Traditional approaches analyze crossings individually, limiting the ability to identify shared behavioral patterns across locations. We propose a multi-view tensor decomposition framework that captures behavioral similarities across three temporal phases: Approach (warning activation to gate lowering), Waiting (gates down to train passage), and Clearance (train passage to gate raising). We analyze railway crossing videos from multiple locations using TimeSformer embeddings to represent each phase. By constructing phase-specific similarity matrices and applying non-negative symmetric CP decomposition, we discover latent behavioral components with distinct temporal signatures. Our tensor analysis reveals that crossing location appears to be a stronger determinant of behavior patterns than time of day, and that approach-phase behavior provides particularly discriminative signatures. Visualization of the learned c...
