Research Of Vehicle Detection Algorithm Based On Vision

In Europe, Japan and other countries and regions, Intelligent Transportation Systems technology is developing rapidly. As the core technology, the vehicle detection is an important research direction. In China, with the increase in car ownership, traffic accidents causing casualties and property losses shocking number, Driver Assistance Systems research more and more attention at home and abroad. The vehicle driving test as an auxiliary of the most important and most basic part of its study is necessary, which involves pattern recognition, image processing, computer vision, machine learning, applied mathematics and other disciplines.At present, the vision-based vehicle detection algorithms mostly follow the two-step procedure:Hypothesis Generation and Hypothesis Verification. These vehicle detection algorithms are mainly research in the context of vehicle detection during the daytime, while for the harsh environment (cloudy, rainy day, reflective, backlight, fog, etc) and nighttime environment, these algorithms are basically unusable. To solve this problem, this paper on the basis of summarizing the applicability of various algorithms, respectively proposed new vehicle detection algorithms for daytime harsh environment and nighttime environment.This paper proposes a new vehicle detection algorithm in daytime harsh environment: In the Hypothesis Generation stage, firstly, we use the Hough transform and vote algorithm to locate the vanishing line, and then in the image plane under vanishing line, we extract the vehicle candidate area based on some criteria; In Hypothesis Verification stage, this paper verification the candidate region by the method of combining the knowledge and the appearance of vehicles, to achieve this goal, we first use image standard deviation eliminate low contrast background, and then use AdaBoost cascade classifier to vehicles in the area of candidate classification, finally, we use the image level further screening of gradient. The experiment results show that this method is not only stable to different illumination conditions, but also improves vehicle detection rate and reduce false alarming rate at the same time.This paper proposes a new vehicle detection algorithm under the environment of night: we first extract the bright areas through the morphological processing method, then match the bright areas based on certain prior knowledge to eliminate the street lamps, so we get the potential lights in pairs; Then we use the Pyramid L-K tracking algorithm to track every potential lamp, according to the similarity movement, we get rid of false lamp matching results, finally we use the right matching lamps to detect the vehicles. The experiment results show that this method is effective to improve the veracity and reliability of vehicle detection under the nighttime.