Anomalous behavior detection is a challenging research area within computer
vision. Progress in this area enables automated detection of dangerous behavior
using surveillance camera feeds. A dangerous behavior that is often overlooked
in other research is the throwing action in traffic flow, which is one of the
unique requirements of our Smart City project to enhance public safety. This
paper proposes a solution for throwing action detection in surveillance videos
using deep learning. At present, datasets for throwing actions are not publicly
available. To address the use-case of our Smart City project, we first generate
the novel public 'Throwing Action' dataset, consisting of 271 videos of
throwing actions performed by traffic participants, such as pedestrians,
bicyclists, and car drivers, and 130 normal videos without throwing actions.
Second, we compare the performance of different feature extractors for our
anomaly detection method on the UCF-Crime and Throwing-Action datasets. The
explored feature extractors are the Convolutional 3D (C3D) network, the
Inflated 3D ConvNet (I3D) network, and the Multi-Fiber Network (MFNet).
Finally, the performance of the anomaly detection algorithm is improved by
applying the Adam optimizer instead of Adadelta, and proposing a mean normal
loss function that covers the multitude of normal situations in traffic. Both
aspects yield better anomaly detection performance. Besides this, the proposed
mean normal loss function lowers the false alarm rate on the combined dataset.
The experimental results reach an area under the ROC curve of 86.10 for the
Throwing-Action dataset, and 80.13 on the combined dataset, respectively.

该研究论文提出了一个基于深度学习的解决方案，用于监控视频中的投掷行为检测，并通过生成新的公开数据集和比较不同特征提取器的性能来改善异常检测算法，实验结果显示了良好的性能。

监控视频中的物体投掷行为检测

Detection of Object Throwing Behavior in Surveillance Videos

Given a road network and a set of trajectory data, the anomalous behavior
detection (ABD) problem is to identify drivers that show significant
directional deviations, hardbrakings, and accelerations in their trips. The ABD
problem is important in many societal applications, including Mild Cognitive
Impairment (MCI) detection and safe route recommendations for older drivers.
The ABD problem is computationally challenging due to the large size of
temporally-detailed trajectories dataset. In this paper, we propose an
Edge-Attributed Matrix that can represent the key properties of
temporally-detailed trajectory datasets and identify abnormal driving
behaviors. Experiments using real-world datasets demonstrated that our approach
identifies abnormal driving behaviors.

给定一个道路网络和一组轨迹数据，本文介绍了一种可以表示详细时空轨迹数据的边属性矩阵，并识别异常驾驶行为的方法。实验结果表明，该方法能够有效地识别异常驾驶行为。

老年驾驶员轨迹数据异常行为检测

Anomalous Behavior Detection in Trajectory Data of Older Drivers

Deep learning has solved a problem that as little as five years ago was
thought by many to be intractable - the automatic recognition of patterns in
data; and it can do so with accuracy that often surpasses human beings. It has
solved problems beyond the realm of traditional, hand-crafted machine learning
algorithms and captured the imagination of practitioners trying to make sense
out of the flood of data that now inundates our society. As public awareness of
the efficacy of DL increases so does the desire to make use of it. But even for
highly trained professionals it can be daunting to approach the rapidly
increasing body of knowledge produced by experts in the field. Where does one
start? How does one determine if a particular model is applicable to their
problem? How does one train and deploy such a network? A primer on the subject
can be a good place to start. With that in mind, we present an overview of some
of the key multilayer ANNs that comprise DL. We also discuss some new automatic
architecture optimization protocols that use multi-agent approaches. Further,
since guaranteeing system uptime is becoming critical to many computer
applications, we include a section on using neural networks for fault detection
and subsequent mitigation. This is followed by an exploratory survey of several
application areas where DL has emerged as a game-changing technology: anomalous
behavior detection in financial applications or in financial time-series
forecasting, predictive and prescriptive analytics, medical image processing
and analysis and power systems research. The thrust of this review is to
outline emerging areas of application-oriented research within the DL community
as well as to provide a reference to researchers seeking to use it in their
work for what it does best: statistical pattern recognition with unparalleled
learning capacity with the ability to scale with information.

本文概述了深度学习的一些关键多层人工神经网络以及使用多智能体方法的新型自动体系结构优化协议；以及深度学习在异常行为检测、预测和预测分析、医学图像处理和分析、电力系统研究等应用领域的探索性调查。