Automated Approach for Computer Vision-based Vehicle Movement Classification at Traffic Intersections

TL;DR

This paper presents an automated, unsupervised computer vision-based method for classifying vehicle movements at traffic intersections, eliminating manual region specification and adapting to various traffic scenarios.

Contribution

It introduces a novel automated classification framework using hierarchical clustering and a new similarity measure for vehicle trajectory analysis.

Findings

Effective classification of vehicle movements without manual intervention

High adaptability to different traffic scenarios

Improved accuracy over existing methods

Abstract

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.