  Movement specific vehicle classification and counting at traffic
intersections is a crucial component for various traffic management activities.
In this context, with recent advancements in computer-vision based techniques,
cameras have emerged as a reliable data source for extracting vehicular
trajectories from traffic scenes. However, classifying these trajectories by
movement type is quite challenging as characteristics of motion trajectories
obtained this way vary depending on camera calibrations. Although some existing
methods have addressed such classification tasks with decent accuracies, the
performance of these methods significantly relied on manual specification of
several regions of interest. In this study, we proposed an automated
classification method for movement specific classification (such as right-turn,
left-turn and through movements) of vision-based vehicle trajectories. Our
classification framework identifies different movement patterns observed in a
traffic scene using an unsupervised hierarchical clustering technique
Thereafter a similarity-based assignment strategy is adopted to assign incoming
vehicle trajectories to identified movement groups. A new similarity measure
was designed to overcome the inherent shortcomings of vision-based
trajectories. Experimental results demonstrated the effectiveness of the
proposed classification approach and its ability to adapt to different traffic
scenarios without any manual intervention.

Computer Vision and Pattern Recognition