Driver Gaze Tracking and Eyes Off the Road Detection System

2015 ◽  
Vol 16 (4) ◽  
pp. 2014-2027 ◽  
Author(s):  
Francisco Vicente ◽  
Zehua Huang ◽  
Xuehan Xiong ◽  
Fernando De la Torre ◽  
Wende Zhang ◽  
...  
Keyword(s):  
Detection System ◽  
Road Detection ◽  
Gaze Tracking ◽  
The Road

scholarly journals A Low Cost Vision-Based Road-Following System for Mobile Robots

2018 ◽  
Vol 8 (9) ◽  
pp. 1635 ◽  
Author(s):  
Haojie Zhang ◽  
David Hernandez ◽  
Zhibao Su ◽  
Bo Su
Keyword(s):  
Mobile Robots ◽  
Unscented Kalman Filter ◽  
Detection System ◽  
Tracking System ◽  
Low Cost ◽  
Recognition Rate ◽  
Road Detection ◽  
The Road ◽  
Road Following ◽  
Following Error

Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.

Study on the Road Rigidity Detecting System Based on LabVIEW

2012 ◽  
Vol 562-564 ◽  
pp. 1986-1989
Author(s):  
Shi Feng Yang ◽  
Chun Qia Liu ◽  
Jing Jing Xu
Keyword(s):  
Virtual Instrument ◽  
Detection System ◽  
National Standard ◽  
Road Maintenance ◽  
Single Chip ◽  
Road Detection ◽  
The Road ◽  
Development Platform ◽  
On The Road

As an important road detection system performance parameter, road rigidity was the key link and the evaluation index of the road detection system. During the construction of road infrastructure, the implementation of road worked on the hardness testing was necessary. The American National Instruments (NI)’s virtual instrument software development platform Lab VIEW was used as the system’s development platform. Through the signal collected by the pressure sensor combined with signal conditioning circuits formed by the single chip, functions of various parts were designed to analyze the relevant parameters of the road rigidity. The test data was measured and collected according to national standard methods, at the same time, virtual instrument software and related algorithms were used to analysis of the statistics data and the state of the road hardness would be detected and researched. And thus it provided an important basis for the quality of road management and road maintenance.

scholarly journals Unpaved road detection using optimized log Gabor filter banks

2021 ◽  
Author(s):  
◽  
Pooparat Plodpradista
Keyword(s):  
Gabor Filter ◽  
Detection System ◽  
Detection Accuracy ◽  
Road Detection ◽  
High Confidence ◽  
Detection Range ◽  
Unpaved Road ◽  
The Road ◽  
Look Ahead ◽  
Log Gabor

The revised unpaved road detection system (RURD) is a novel method for detecting unpaved roads in an arid environment from color imagery collected by a forward-looking camera mounted on a moving platform. The objective is to develop and validate a novel system with the ability to detect an unpaved road at a look-ahead distance up to 40 meters that does not utilize an expensive sensor, i.e., LIDAR but instead a low-cost color camera sensor. The RURD system is composed of two stages, the road region estimation (RRE) and the road model formation (RMF). The RRE stage classifies the image patches selected at 20-meter distance from the camera and labels them to either road or non-road. The classification result is used as a high confidence road area in the image, which is used in the RMF stage. The RMF stage uses log Gabor filter bank to extract road pixels that connect to the high confidence road region and generates a 3rd degree polynomial curve to represent the road model in a given image. The road model allows the system to extend the detection range from 20 meters to farther look-ahead distance. The RURD system is evaluated with two-years worth of data collection that measures both spatial and temporal precisions. The system is also benchmarked against an algorithm from Rasmussen entitled "Grouping Dominant Orientations for Ill-Structured Roads Following", which shown an average increase detection accuracy over 30 [percent].

scholarly journals Online Road Detection under a Shadowy Traffic Image Using a Learning-Based Illumination-Independent Image

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 707 ◽  
Author(s):  
Yongchao Song ◽  
Yongfeng Ju ◽  
Kai Du ◽  
Weiyu Liu ◽  
Jiacheng Song
Keyword(s):  
Feature Fusion ◽  
Detection System ◽  
Geometric Mean ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Light Illumination ◽  
Road Detection ◽  
The Road

Shadows and normal light illumination and road and non-road areas are two pairs of contradictory symmetrical individuals. To achieve accurate road detection, it is necessary to remove interference caused by uneven illumination, such as shadows. This paper proposes a road detection algorithm based on a learning and illumination-independent image to solve the following problems: First, most road detection methods are sensitive to variation of illumination. Second, with traditional road detection methods based on illumination invariability, it is difficult to determine the calibration angle of the camera axis, and the sampling of road samples can be distorted. The proposed method contains three stages: The establishment of a classifier, the online capturing of an illumination-independent image, and the road detection. During the establishment of a classifier, a support vector machine (SVM) classifier for the road block is generated through training with the multi-feature fusion method. During the online capturing of an illumination-independent image, the road interest region is obtained by using a cascaded Hough transform parameterized by a parallel coordinate system. Five road blocks are obtained through the SVM classifier, and the RGB (Red, Green, Blue) space of the combined road blocks is converted to a geometric mean log chromatic space. Next, the camera axis calibration angle for each frame is determined according to the Shannon entropy so that the illumination-independent image of the respective frame is obtained. During the road detection, road sample points are extracted with the random sampling method. A confidence interval classifier of the road is established, which could separate a road from its background. This paper is based on public datasets and video sequences, which records roads of Chinese cities, suburbs, and schools in different traffic scenes. The author compares the method proposed in this paper with other sound video-based road detection methods and the results show that the method proposed in this paper can achieve a desired detection result with high quality and robustness. Meanwhile, the whole detection system can meet the real-time processing requirement.

Real-time unstructured road detection based on CNN and Gibbs energy function

2021 ◽  
pp. 1-19
Author(s):  
Mingzhou Liu ◽  
Xin Xu ◽  
Jing Hu ◽  
Qiannan Jiang
Keyword(s):  
Gibbs Energy ◽  
Energy Function ◽  
Gaussian Mixture Models ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Gibbs Energy Function ◽  
Road Detection ◽  
The Road ◽  
Detection Algorithms ◽  
Random Sample Consensus

Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels.

Road Detection Based on Edge Feature with GAC Model in Aerial Image

2021 ◽  
Author(s):  
Linying Zhou ◽  
Zhou Zhou ◽  
Hang Ning
Keyword(s):  
Transformation Method ◽  
Aerial Images ◽  
Aerial Image ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Road Detection ◽  
Canny Operator ◽  
The Road ◽  
Gradient Magnitude ◽  
Edge Feature

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

scholarly journals Fusing Appearance and Prior Cues for Road Detection

2019 ◽  
Vol 9 (5) ◽  
pp. 996
Author(s):  
Fenglei Ren ◽  
Xin He ◽  
Zhonghui Wei ◽  
Lei Zhang ◽  
Jiawei He ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Autonomous Driving ◽  
Relevant Information ◽  
Gaussian Mixture ◽  
Morphological Processing ◽  
Road Detection ◽  
Collision Warning ◽  
The Road ◽  
Illuminant Invariant ◽  
Institute Of Technology

Road detection is a crucial research topic in computer vision, especially in the framework of autonomous driving and driver assistance. Moreover, it is an invaluable step for other tasks such as collision warning, vehicle detection, and pedestrian detection. Nevertheless, road detection remains challenging due to the presence of continuously changing backgrounds, varying illumination (shadows and highlights), variability of road appearance (size, shape, and color), and differently shaped objects (lane markings, vehicles, and pedestrians). In this paper, we propose an algorithm fusing appearance and prior cues for road detection. Firstly, input images are preprocessed by simple linear iterative clustering (SLIC), morphological processing, and illuminant invariant transformation to get superpixels and remove lane markings, shadows, and highlights. Then, we design a novel seed superpixels selection method and model appearance cues using the Gaussian mixture model with the selected seed superpixels. Next, we propose to construct a road geometric prior model offline, which can provide statistical descriptions and relevant information to infer the location of the road surface. Finally, a Bayesian framework is used to fuse appearance and prior cues. Experiments are carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road benchmark where the proposed algorithm shows compelling performance and achieves state-of-the-art results among the model-based methods.

On-the-Road Detection of Driving While Intoxicated

1979 ◽  
Vol 23 (1) ◽  
pp. 263-266
Author(s):  
Douglas H. Harris
Keyword(s):  
Law Enforcement ◽  
Police Officers ◽  
Visual Cues ◽  
Frequency Of Occurrence ◽  
Road Detection ◽  
Patrol Officers ◽  
Law Enforcement Agencies ◽  
Driving While Intoxicated ◽  
The Road ◽  
On The Road

Visual cues were identified and procedures were developed to enhance on-the-road detection of driving while intoxicated (DWI) by police patrol officers. Related research was reviewed; police officers with demonstrated effectiveness in DWI detection were interviewed; DWI arrest reports were analyzed; and a study was conducted to determine the frequency of occurrence and relative discriminability of potential visual cues. Based on the results, a DWI detection Guide was developed; the Guide is currently being verified and evaluated in a field-study involving a sample of 10 law enforcement agencies.

Sign in / Sign up

Export Citation Format

Share Document