A Road Detection Algorithm by Boosting Using Feature Combination

Sha, Yun; Zhang, Guoying; Yang, Yong

doi:10.1109/ivs.2007.4290141

Cited by 24 publications

(8 citation statements)

References 5 publications

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“…Our proposed method for identifying a departure, finds the coordinate of the lowest point of right and left lines of the current lane respectively as (Xr,Yr) and (Xl,Yl), then we use Xr and Xl to calculate a distance measurement as follow: (2) This measurement calculates the distance between the lowest point of each line of the current lane and center of the image width. If the CenterDistance is lower than a certain threshold, we would have a departure.…”

Section: Lane Deprature Warningmentioning

confidence: 99%

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Current and adjacent lanes detection for an autonomous vehicle to facilitate obstacle avoidance using a monocular camera

Kalaki

Safabakhsh

2014

2014 Iranian Conference on Intelligent Systems (ICIS)

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In order to gain higher efficiency in obstacle avoidance task in autonomous vehicles from the aspect of processing cost and operating in real-time, it`s critical to find a region of interest (ROI) which obstacles are more possible to appear and degrade the obstacle`s search zone to it. In this paper we propose novel methods to find this ROI using computer vision technologies. The road scenes are acquired with a monocular camera. Current lane of autonomous vehicle is recognized by detection of lane markings. Adjacent lanes are also estimated based on some geometric calculations. A novel lane matching mechanism is suggested to validate detected lane markings. Finally a method for lane departure warning is proposed. The experimental results show that the proposed algorithms correctly find lanes region with high accuracy in real-time, are robust to noise and shadows, testing on Hemmat highway in Tehran and another dataset in the daytime.

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Section: Lane Deprature Warningmentioning

confidence: 99%

“…In [2], the road detection is performed by boosting image features. In [3], the road detection approach uses Illuminant Invariance Theory on color images to classify road pixels.…”

Section: Introductionmentioning

confidence: 99%

Current and adjacent lanes detection for an autonomous vehicle to facilitate obstacle avoidance using a monocular camera

Kalaki

Safabakhsh

2014

2014 Iranian Conference on Intelligent Systems (ICIS)

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“…For example, Zhou et al [22] proposed an effective approach to use SVM for road detection with self-supervised online learning. Yun et al [23] adopted the boosting, SVM, and random forest classifiers to evaluate the correlation feature set and raw feature set. To fully utilize potential region feature correlations and improve the classification accuracy, this algorithm also introduces the feature combination method into road detection.…”

Section: Introductionmentioning

confidence: 99%

Unpaved road detection based on spatial fuzzy clustering algorithm

Bao

Zhang

et al. 2018

J Image Video Proc.

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Vision-based unpaved road detection is a challenging task due to the complex nature scene. In this paper, a novel algorithm is proposed to improve the accuracy and robustness of unpaved road detection and boundary extraction with low computational costs. The novelties of this paper are as follows: (1) We use a normal distribution with infrared images to detect the vanishing line, and a trapezoid prediction model is proposed according to the road shape features. (2) Road recognition based on connected regions is implemented by an improved support vector machine (SVM) classifier with a normalized class feature vector. According to the recognition results, the road probability confidence map is obtained. (3) With the help of fusing continuous information with the trapezoidal forecasting model and the probability from the confidence map, we present a road probability recognition method based on the trapezoidal forecasting model and spatial fuzzy clustering. Furthermore, the histogram backprojection model is used to solve interference problems caused by shadows on the road. It takes approximately 0.012~0.014 s to process one frame of an image for the road recognition, and the accuracy rate can reach 93.2%. The experimental results show that the algorithm can achieve better performance than some state-of-the-art methods in terms of detection accuracy and speed.

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“…In [1], the road detection is performed by boosting image features. In [2], the authors propose to detect the road using a new approach for vanishing points detection combined with texture orientations extraction.…”

Section: Introductionmentioning

confidence: 99%

Fast road detection from color images

Wang

Frémont

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this paper, we present a method for drivable road detection by extracting its specular intrinsic feature from an image. The resulting detection is then used in a stereo vision-based 3D road parameters extraction algorithm. A substantial representation of the road surface, called axis-calibration, is represented as an angle in logchromaticity space. This feature provides an invariance to road surface under illuminant conditions with shadow or not. We also add a sky removal function in order to eliminate the negative effects of sky light on axiscalibration result. Then, a confidence interval calculation helps the pixels' classification to speed up the detection processing. At last, the approach is combined with a stereovision based method to filter out false detected pixels and to obtain precise 3D road parameters. The experimental results show that the proposed approach can be adapted for real-time ADAS system in various driving conditions.

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A Road Detection Algorithm by Boosting Using Feature Combination

Cited by 24 publications

References 5 publications

Current and adjacent lanes detection for an autonomous vehicle to facilitate obstacle avoidance using a monocular camera

Current and adjacent lanes detection for an autonomous vehicle to facilitate obstacle avoidance using a monocular camera

Unpaved road detection based on spatial fuzzy clustering algorithm

Fast road detection from color images

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