Abstract
A robust vision-based traffic monitoring system for vehicle and traffic information extraction is developed in this research. It is challenging to maintain detection robustness at all time for a highway surveillance system. There are three major problems in detecting and tracking a vehicle: (1) the moving cast shadow effect, (2) the occlusion effect, and (3) nighttime detection. For moving cast shadow elimination, a 2D joint vehicle-shadow model is employed. For occlusion detection, a multiple-camera system is used to detect occlusion so as to extract the exact location of each vehicle. For vehicle nighttime detection, a rear-view monitoring technique is proposed to maintain tracking and detection accuracy. Furthermore, we propose a method to improve the accuracy of background extraction, which usually serves as the first step in any vehicle detection processing. Experimental results are given to demonstrate that the proposed techniques are effective and efficient for vision-based highway surveillance.
Highlights
Vision-based traffic monitoring systems are widely used in intelligent transportation systems (ITS)
We investigate techniques to enhance the performance robustness of highway surveillance systems in the areas of cast shadow elimination, occlusion detection, and nighttime detection
We evaluated the performance of a vision-based highway surveillance system by implementing the proposed shadow elimination, occlusion detection, vehicle nighttime detection, and enhanced background maintenance techniques using various highway traffic scenes captured with a fixed camera position
Summary
Vision-based traffic monitoring systems are widely used in intelligent transportation systems (ITS). Several different types of devices, including loop detectors, sensors, and cameras, have been employed in traffic monitoring systems. Vision-based analysis systems have become popular in transportation management due to their capability to extract a wide variety of information in comparison with the sensor-based system. It is challenging to maintain detection accuracy at all time since vision-based processing is sensitive to environmental factors such as lighting, shadow, and weather conditions. This algorithm does not perform 3D image analysis operations so that the complexity is low. Our approach has several advantages in comparison with conventional approaches This approach does not need edge detection or region segmentation operations that are sensitive to environmental factors. We present a background maintenance method that can adjust the background model according to the environmental change quickly
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