Video feeds from traffic cameras can be useful for many purposes, the most critical of which are related to monitoring
road safety. Vehicle trajectory is a key element in dangerous behavior and traffic accidents. In this respect, it is crucial to detect
those anomalous vehicle trajectories, that is, trajectories that depart from usual paths. In this work, a model is proposed to
automatically address that by using video sequences from traffic cameras. The proposal detects vehicles frame by frame, tracks
their trajectories across frames, estimates velocity vectors, and compares them to velocity vectors from other spatially adjacent
trajectories. From the comparison of velocity vectors, trajectories that are very different (anomalous) from neighboring trajectories
can be detected. In practical terms, this strategy can detect vehicles in wrong-way trajectories. Some components of the model are
off-the-shelf, such as the detection provided by recent deep learning approaches; however, several different options are considered
and analyzed for vehicle tracking. The performance of the system has been tested with a wide range of real and synthetic traffic
videos.
