Real-time daytime road marker recognition using features vectors and neural network

Abstract

Road markers provide vital information to ensure traffic safety. Different sets of markers are normally used between the highways and the normal road. At the normal road for example, the double lane markers are used to indicate the hazardous area, where overtaking is prohibited while broken marker lane indicate otherwise. To avoid traffic accidents and provide safety, these markers should be accurately detected and classified, which is best solved via vision detection approach. Marker type classification is however affected by the changing sun illumination throughout the day. In this paper, real-time recognition of these markers is developed using the artificial neural network (ANN) to alert the users while driving. The accuracy of the scheme is observed when different input features (geometrical and texture) and image pixels are fed for recognizing broken and double lane markers. A very high accuracy result with low error rate is obtained at 98.83% (10-fold cross validation) accuracy detection using additional features, compared with ~95% by using only the image pixels as the input vector and average processing time is at ~30ms per frame.

Advances in lane marking detection algorithms for all-weather conditions

“…Based on the five common types of lane markers on the road [12][13][14], the number of contour lines ranges from zero to four. For example, the number of contour lines for marker S is two and D is four.…”

“…An accuracy value of 100% is recorded in two different video clips, which are Clip1 and Clip2Test. The proposed method has also been tested with different datasets including those applied in [12][13][14]47…”

“…In order to classify the lane markers, the features which are used in the decision rule need to be determined. In literature, some of the features that have been used include edges [26], histograms [27] and other geometrical features including lines, curves and contours [13,14]. The selection of these features depends on the number of different types of lane markers targeted to be classified, besides the temporal and computational loads that need to be minimized and controlled.…”

“…Lane marker classification is an essential part of the lane detection mechanisms [11,12] to assist the drivers making the right decisions as well as to enhance the advanced driver assistance systems. A lane marker classification method using contour analysis has been proposed in [13,14]. This research paper presents a comprehensive review on road marking classification mechanisms applied in rainy and foggy weather conditions with an initial framework of lane marker classification mechanism for all-weather conditions.…”

A review on sparse fast fourier transform applications in image processing

“…Fourier Transform is first introduced by Jean Baptiste Joseph Fourier [1] to solve the computational complexity in wide varities of fields including earth and science, chemistry, communications, and signal processing [2][3][4][5]. In signal processing, Fourier Transform [6][7][8][9][10][11] has long been established as an instrumental tool applied in electrical signal spectrum and filter analysis, sampling and series, antenna, television image convolution as well as radio broadcasting [1]. Being the limiting case of Fourier Series for non-periodic signals, FT is used to convert signal to frequency domain as the frequency domain has many superlative benefits especially for analytical purposes rather than in the classical time domain.…”