  Traffic Management Centers (TMCs) routinely use traffic cameras to provide
situational awareness regarding traffic, road, and weather conditions. Camera
footage is quite useful for a variety of diagnostic purposes; yet, most footage
is kept for only a few days, if at all. This is largely due to the fact that
currently, identification of notable footage is done via manual review by human
operators---a laborious and inefficient process. In this article, we propose a
semantics-oriented approach to analyzing sequential image data, and demonstrate
its application for automatic detection of real-world, anomalous events in
weather and traffic conditions. Our approach constructs semantic vector
representations of image contents from textual labels which can be easily
obtained from off-the-shelf, pretrained image labeling software. These semantic
label vectors are used to construct semantic topic signals---time series
representations of physical processes---using the Latent Dirichlet Allocation
(LDA) topic model. By detecting anomalies in the topic signals, we identify
notable footage corresponding to winter storms and anomalous traffic
congestion. In validation against real-world events, anomaly detection using
semantic topic signals significantly outperforms detection using any individual
label signal.
