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Currently, there are computer vision systems that help us with tasks thatwould be dull for humans, such as surveillance and vehicle tracking. Animportant part of this analysis is to identify traffic anomalies. An anomalytells us that something unusual has happened, in this case on the highway. Thispaper aims to model vehicle tracking using computer vision to detect trafficanomalies on a highway. We develop the steps of detection, tracking, andanalysis of traffic: the detection of vehicles from video of urban traffic, thetracking of vehicles using a bipartite graph and the Convex Hull algorithm todelimit moving areas. Finally for anomaly detection we use two data structuresto detect the beginning and end of the anomaly. The first is the QuadTree thatgroups vehicles that are stopped for a long time on the road and the secondthat approaches vehicles that are occluded. Experimental results show that ourmethod is acceptable on the Track4 test set, with an F1 score of 85.7% and amean squared error of 25.432.

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic