Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
This work addresses safety challenges at railway crossings by enabling scalable pattern discovery to inform targeted interventions, though it is incremental as it builds on existing tensor methods and video analysis techniques.
The paper tackled the problem of identifying shared driver behavior patterns at railway crossings by proposing a multi-view tensor decomposition framework that analyzes videos across three temporal phases, revealing that crossing location is a stronger determinant of behavior than time of day and that approach-phase behavior is particularly discriminative.
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 component space confirms location-based clustering, with certain crossings forming distinct behavioral clusters. This automated framework enables scalable pattern discovery across multiple crossings, providing a foundation for grouping locations by behavioral similarity to inform targeted safety interventions.