Landmark-Based Features for Vehicle Trajectory Anomaly Detection from Traffic Video in Urban Intersections—A Case Study

We study trajectory feature representations in the context of detecting spatially anomalous vehicle trajectories in urban intersections, using trajectory data from video streams captured by camera monitoring systems. These trajectories are extracted using an object detection pipeline and have particular characteristics like short lengths, variable endpoints, and other viewpoint-dependent detection artifacts, which make existing spatial feature approaches less effective. We introduce two feature representations adapted for intersection-level trajectories, based on distances to a fixed set of landmark points, which provide fixed-length vectors compatible with common tabular anomaly detector algorithms. We evaluate using a dataset of 5378 labeled trajectories collected from camera recordings in one deployment site, as well as on other existing city-wide benchmark datasets, showing that, in the evaluated setting, the proposed feature representations improve upon several existing spatial features and enable better detection of both shape and placement anomalies.