24-02-2016, 03:22 PM
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.
INTRODUCTION
VIDEO cameras could be used to record the traffic information constantly or continuously. We are thus able to analyze the traffic videos in real time and discover any information of interest. One fundamental task is to count the vehicles passing by in a given time period and classify the vehicles into different categories at the same time. The counting and classification results could be useful in many different applications. For example, they could be used to measure traffic density, traffic flow, and even emissions in terms of pollutants and greenhouse gases. Counting and classification also could be done by other sensors such as radar, infrared, and inductive loop detectors. Although some sensors could be more accurate, they could also be intrusive and need a higher maintenance cost. For example, we may need to embed weighing sensors in road to measure vehicle weight and classify vehicle size. Comparing with other sensors, vision-based systems could be non-intrusive and could obtain much richer traffic information. However, current visionbased systems could be less accurate and more sensitive to operating conditions (e.g., weather). These problems make vision-based systems challenging and important research topics in the area of intelligent transportation systems.