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 , 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.
Highlights
The global trend toward urbanization has experienced a worrying growth over the last 30 years, contributing to make cities socially, economically and environmentally unsustainable
The set of test videos have more than 2340 s of traffic scenes previously recorded from a surveillance camera and a smart phone with a resolution of 420 × 240 pixels and a frame rate of 25 frames per second (FPS)
The lane detection algorithm was implemented in Matlab running on a dual core 2.4GHz intel core i5 machine with 8GB of RAM
Summary
The global trend toward urbanization has experienced a worrying growth over the last 30 years, contributing to make cities socially, economically and environmentally unsustainable. Intelligent Transportation Systems (ITS) have been developed. Applications such as traffic control systems, Closed-Circuit Television (CCTV) security systems, speed cameras, plate recognition technologies, and automatic lane detection are some examples of ITS. Which satisfies some background criterion [23], e.g., the statistical mode of each xt (i, j) at which the Probability Mass Function (pmf) takes its maximum. Any object moving across the background will change xt (i, j) for all its associated pixels and almost always the histogram as well. Its associated pixel pmf f X ( x ) is estimated and updated (to have a fair comparison of each pixel pmf, the number of bins or classes of each histogram is fixed and equal for all). Histograms are the basis for the background image formation (see Section 4.2.2)
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