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 traffic agents in bird's-eye-view
videos of traffic cameras mounted at an intersection. By conceptualizing the
intersection as a patch-based graph, it is shown that the framework learns and
models the normal behaviour of traffic agents without costly manual labeling.
Furthermore, uTRAND allows to formulate simple rules to classify anomalous
trajectories in a way suited for human interpretation. We show that uTRAND
outperforms other state-of-the-art approaches on a dataset of anomalous
trajectories collected in a real-world setting, while producing explainable
detection results.

uTRAND 是一种深度学习技术的框架，通过将异常轨迹预测问题从像素空间转移到语义拓扑领域，检测和跟踪交通摄像头中各种类型的交通代理。通过将交叉口视为基于补丁的图形，该框架能够在没有昂贵手工分类的情况下学习和建模交通代理的正常行为，并且能够制定简单规则以便于人类解释异常轨迹。在真实环境中收集的异常轨迹数据集上，uTRAND 在性能和可解释性方面都优于其他先进方法。