Inexpensive sensing and computation, as well as insurance innovations, have
made smart dashboard cameras ubiquitous. Increasingly, simple model-driven
computer vision algorithms focused on lane departures or safe following
distances are finding their way into these devices. Unfortunately, the
long-tailed distribution of road hazards means that these hand-crafted
pipelines are inadequate for driver safety systems. We propose to apply
data-driven anomaly detection ideas from deep learning to dashcam videos, which
hold the promise of bridging this gap. Unfortunately, there exists almost no
literature applying anomaly understanding to moving cameras, and
correspondingly there is also a lack of relevant datasets. To counter this
issue, we present a large and diverse dataset of truck dashcam videos, namely
RetroTrucks, that includes normal and anomalous driving scenes. We apply: (i)
one-class classification loss and (ii) reconstruction-based loss, for anomaly
detection on RetroTrucks as well as on existing static-camera datasets. We
introduce formulations for modeling object interactions in this context as
priors. Our experiments indicate that our dataset is indeed more challenging
than standard anomaly detection datasets, and previous anomaly detection
methods do not perform well here out-of-the-box. In addition, we share insights
into the behavior of these two important families of anomaly detection
approaches on dashcam data.

本文提出了一种基于深度学习的异常检测方法，通过数据集 RetroTrucks 以及静态摄像头数据集对单类分类损失和重构损失的应用，以及对象交互建模，实现对车载摄像头录像中不同驾驶行为的检测和识别。