Isotropic Coordinate Normalization and Target-Aware Search for Vehicle Trajectory Clustering at Complex Urban Intersections

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Abstract
Grouping vehicles with similar paths is important for traffic analysis, but camera-image trajectories are distorted by perspective, and unsupervised clustering does not directly reveal how many traffic movements should be expected. This paper presents Homography-Guided Semantic Maneuver Graph Trajectory Clustering (HG-SMG-TC), a model-selection framework that uses a lightweight homography to estimate intersection structure while retaining isotropically normalized camera coordinates for clustering. Entry and exit endpoint groups are consolidated into physical approach-level groups, and their supported origin–destination relationships form a maneuver graph. Bootstrap resampling converts this structure into an interval for the expected number of observed movements, which guides clustering model selection. The method is evaluated on 67,029 vehicle trajectories from five urban intersection scenes in the Traffic Node Video Dataset, using separate target-estimation, model-selection, and independent-test recording blocks. Independent polygon-rule reference labels cover 89.6–98.4% of test trajectories. HG-SMG-TC reduces mean target-count error from 3.20 for untargeted selection and 2.53 for the point-target variant to 2.13, while achieving an adjusted Rand index of 0.734 and normalized mutual information of 0.839. The results show that the proposed semantic maneuver prior improves target alignment and provides a reproducible way to guide unsupervised trajectory clustering, while retaining explicit trade-offs across evaluation metrics.

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