Traffic volume measurement using nonlinear count-lines

Abstract Traffic volume measurements are increasingly recognized as important for improving society’s infrastructure, by functions such as managing congestion and enhancing logistics. Dedicated traffic data capture devices that use sensors embedded in the road enable accurate measurements but have the problems of high cost and limited installation locations, which make it difficult to expand the coverage of traffic volume measurements. To address this issue, the approach that combines already deployed Closed-Circuit Television (CCTV) cameras with image recognition technology has attracted attention and offers practical performance in ordinary situations. One remaining problem is that accuracy is degraded by the presence of headlight flare at nighttime and occlusion by large vehicles on busy roads. In this paper, we propose a method for measuring traffic volume that automatically sets count-lines using the Kernel Support Vector Machine (Kernel SVM) at optimal positions less affected by these issues. In addition, to make the proposal robust to illumination changes and occlusion we introduce nonlinear count-lines. Extensive experiments on Japanese road video footage shows that our method improves accuracy by $$5.9\%$$ at night and $$2.1\%$$ in situations prone to occlusion compared to the most basic fixed count-line method. Additionally, experiments on a public dataset, UA-DETRAC, demonstrate the proposal’s effectiveness in countries other than Japan.