Counting and Classification of Highway Vehicles by Regression Analysis

Abstract

In this paper, we describe a novel algorithm that counts and classifies highway vehicles based on regression analysis. This algorithm requires no explicit segmentation or tracking of individual vehicles, which is usually an important part of many existing algorithms. Therefore, this algorithm is particularly useful when there are severe occlusions or vehicle resolution is low, in which extracted features are highly unreliable. There are mainly two contributions in our proposed algorithm. First, a warping method is developed to detect the foreground segments that contain unclassified vehicles. The common used modeling and tracking (e.g., Kalman filtering) of individual vehicles are not required. In order to reduce vehicle distortion caused by the foreshortening effect, a nonuniform mesh grid and a projective transformation are estimated and applied during the warping process. Second, we extract a set of low-level features for each foreground segment and develop a cascaded regression approach to count and classify vehicles directly, which has not been used in the area of intelligent transportation systems. Three different regressors are designed and evaluated. Experiments show that our regression-based algorithm is accurate and robust for poor quality videos, from which many existing algorithms could fail to extract reliable features.