MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial
  and temporal structures in vehicle traffic
Ana Rosalía Huamán Reyna Alex Josué Flórez Farfán Geraldo Pereira Rocha Filho Sandra Sampaio Robson de Grande Luis Hideo Vasconcelos Nakamura Rodolfo Ipolito Meneguette
Currently, there are computer vision systems that help us with tasks that
would be dull for humans, such as surveillance and vehicle tracking. An
important part of this analysis is to identify traffic anomalies. An anomaly
tells us that something unusual has happened, in this case on the highway. This
paper aims to model vehicle tracking using computer vision to detect traffic
anomalies on a highway. We develop the steps of detection, tracking, and
analysis of traffic: the detection of vehicles from video of urban traffic, the
tracking of vehicles using a bipartite graph and the Convex Hull algorithm to
delimit moving areas. Finally for anomaly detection we use two data structures
to detect the beginning and end of the anomaly. The first is the QuadTree that
groups vehicles that are stopped for a long time on the road and the second
that approaches vehicles that are occluded. Experimental results show that our
method is acceptable on the Track4 test set, with an F1 score of 85.7% and a
mean squared error of 25.432.