  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.
