A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation
| Autor: | Jayro Santiago-Paz, Julio César Ramírez-Pacheco, Deni Torres-Roman, Fernando Hermosillo-Reynoso |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
surveillance systems
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Physics and Astronomy Poison control lcsh:Astrophysics 02 engineering and technology Article automatic lane detection Histogram lcsh:QB460-466 0502 economics and business Computer Science::Multimedia 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Rectangle lcsh:Science Intelligent transportation system 050210 logistics & transportation dtw k-means Pixel 05 social sciences Shannon entropy 020206 networking & telecommunications lcsh:QC1-999 Maxima and minima Tsallis entropy entropic index q Computer Science::Computer Vision and Pattern Recognition lcsh:Q time series Row Algorithm lcsh:Physics |
| Zdroj: | Entropy Volume 20 Issue 10 Entropy, Vol 20, Iss 10, p 725 (2018) |
| ISSN: | 1099-4300 |
| DOI: | 10.3390/e20100725 |
| Popis: | 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 &tau 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|