Urban anomaly predictions, such as traffic accident prediction and crime
prediction, are of vital importance to smart city security and maintenance.
Existing methods typically use deep learning to capture the intra-dependencies
in spatial and temporal dimensions. However, numerous key challenges remain
unsolved, for instance, sparse zero-inflated data due to urban anomalies
occurring with low frequency (which can lead to poor performance on real-world
datasets), and both intra- and inter-dependencies of abnormal patterns across
spatial, temporal, and semantic dimensions. Moreover, a unified approach to
predict multiple kinds of anomaly is left to explore. In this paper, we propose
STS to jointly capture the intra- and inter-dependencies between the patterns
and the influential factors in three dimensions. Further, we use a multi-task
prediction module with a customized loss function to solve the zero-inflated
issue. To verify the effectiveness of the model, we apply it to two urban
anomaly prediction tasks, crime prediction and traffic accident risk
prediction, respectively. Experiments on two application scenarios with four
real-world datasets demonstrate the superiority of STS, which outperforms
state-of-the-art methods in the mean absolute error and the root mean square
error by 37.88% and 18.10% on zero-inflated datasets, and, 60.32% and 37.28% on
non-zero datasets, respectively.

该研究提出了一个名为 STS 的多任务预测模型，可以联合三维空间、时间和语义方面的异常模式与影响因素间的内部和互间关系，并使用定制的损失函数解决由于稀疏零膨胀数据引起的问题，从而有效预测城市安全和维护方面的多种异常情况。通过四个实际数据集上的实验验证，该模型在平均绝对误差和均方根误差方面比现有方法表现更优秀。

时空和语义结合的城市异常预测

Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction

We focus on estimating the 3D orientation of the ground plane from a single
image. We formulate the problem as an inter-mingled multi-task prediction
problem by jointly optimizing for pixel-wise surface normal direction, ground
plane segmentation, and depth estimates. Specifically, our proposed model,
GroundNet, first estimates the depth and surface normal in two separate
streams, from which two ground plane normals are then computed
deterministically. To leverage the geometric correlation between depth and
normal, we propose to add a consistency loss on top of the computed ground
plane normals. In addition, a ground segmentation stream is used to isolate the
ground regions so that we can selectively back-propagate parameter updates
through only the ground regions in the image. Our method achieves the
top-ranked performance on ground plane normal estimation and horizon line
detection on the real-world outdoor datasets of ApolloScape and KITTI,
improving the performance of previous art by up to 17.7% relatively.

GroundNet 模型通过多任务联合优化像素级表面法线方向、地面平面分割和深度估计，实现从单张图像中估算出地面平面的 3D 方向，通过加入一致性损失优化了地面平面法线预测，并在 ApolloScape 和 KITTI 等真实世界数据集上取得了顶尖水平。