uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories

arXiv:2404.12712v1 Announce Type: cross
Abstract: Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper addresses two issues: the lack of labeled data and the difficulty of explaining the predictions of a neural network. To this end, we present a framework called uTRAND, that shifts the problem of anomalous trajectory prediction from the pixel space to a semantic-topological domain. The framework detects and tracks all types of …

abstract anomaly anomaly detection applications arxiv cs.ai cs.cv cs.lg data datasets deep learning detection improvements network neural network paper predictions public traffic type unsupervised video world