Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection
This work addresses inefficiencies in traffic management by automating event detection, but it is incremental as it builds on existing methods like LDA and pretrained labeling software.
The authors tackled the problem of manually reviewing traffic camera footage by proposing a semantics-oriented approach to automatically detect anomalous events like winter storms and traffic congestion, achieving significant performance improvements over using individual label signals in validation.
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.