A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation

Abstract

Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period τ, its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value;(2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.

A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC

“…For the static camera sensor, the application scenarios include precise vehicle positioning and intelligent transportation. The main methods include method are based on the trajectories of vehicles [38,39] and pixel-entropy [40]. For the vehicle-mounted camera sensor, the application scenarios include intelligent vehicles and advanced driver assistant systems (ADAS) [41,42].…”

Advances in lane marking detection algorithms for all-weather conditions

“…However, this approach is carried out only for tractor semi-trailer excluding other vehicles. A different approach of detecting lanes at different weather conditions is proposed in [10] based on the entropy approach. The detection accuracy reported for this work is good although this work is implemented using the surveillance camera.…”