Adaptive Vehicle Detector Approach for Complex Environments

Abstract

In this paper, a vehicle detection approach for complex environments is presented. This paper proposes methods for solving problems of vehicle detection in traffic jams and complex weather conditions such as sunny days, rainy days, cloudy days, sunrise time, sunset time, or nighttime. In recent research, there have been many well-known vehicle detectors that utilize background extraction methods to recognize vehicles. In these studies, the background image needs to continuously be updated; otherwise, the luminance variation will impact the detection quality. The vehicle detection under various environments will have many difficulties such as illumination vibrations, shadow effects, and vehicle overlapping problems that appear in traffic jams. The main contribution of this paper is to propose an adaptive vehicle detection approach in complex environments to directly detect vehicles without extracting and updating a reference background image in complex environments. In the proposed approach, histogram extension addresses the removal of the effects of weather and light impact. The gray-level differential value method is utilized to directly extract moving objects from the images. Finally, tracking and error compensation are applied to refine the target tracking quality. In addition, many useful traffic parameters are evaluated. These useful traffic parameters, including traffic flows, velocity, and vehicle classifications, can help to control traffic and provide drivers with good guidance. Experimental results show that the proposed methods are robust, accurate, and powerful enough to overcome complex weather conditions and traffic jams.