A mathematical morphology-based approach for vehicle detection in road tunnels

Abstract

A novel approach to automatically detect vehicles in road tunnels is presented in this paper. Non-uniform and poor illumination conditions prevail in road tunnels making difficult to achieve robust vehicle detection. In order to cope with the illumination issues, we propose a local higher-order statistic filter to make the vehicle detection invariant to illumination changes, whereas a morphological-based background subtraction is used to generate a convex hull segmentation of the vehicles. An evaluation test comparing our approach with a benchmark object detector shows that our approach outperforms in terms of false detection rate and overlap area detection.

Split-and-match: A Bayesian framework for vehicle re-identification in road tunnels

“…Consequently, the remaining 187 vehicles were used for testing. In order to also evaluate our method using automatic detections, we generate them with the vehicle detector proposed in Frías-Velázquez et al (2011).…”