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

• URL: http://arxiv.org/abs/2111.09171v1
• Date: Wed, 17 Nov 2021 15:02:43 GMT
• Title: Automated Approach for Computer Vision-based Vehicle Movement Classification at Traffic Intersections
• Authors: Udita Jana, Jyoti Prakash Das Karmakar, Pranamesh Chakraborty, Tingting Huang, Dave Ness, Duane Ritcher, Anuj Sharma
• Abstract summary: We propose an automated classification method for movement specific classification of vision-based vehicle trajectories. Our framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories.
• Score: 7.3496760394236595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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