@article{ref1,
title="A novel algorithm based on the pixel-entropy for automatic detection of number of  lanes, lane centers, and lane division lines formation",
journal="Entropy (Basel, Switzerland)",
year="2018",
author="Hermosillo-Reynoso, Fernando and Torres-Roman, Deni and Santiago-Paz, Jayro and Ramirez-Pacheco, Julio",
volume="20",
number="10",
pages="e20100725-e20100725",
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.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e20100725",
url="http://dx.doi.org/10.3390/e20100725"
}