SEMANTIC INFORMATION EXTRACTION OF LANES BASED ON ONBOARD CAMERA VIDEOS

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

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Improving Information Extraction from Images with Learned Semantic Models

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/724 ◽

2018 ◽

Cited By ~ 1

Author(s):

Stephan Baier ◽

Yunpu Ma ◽

Volker Tresp

Keyword(s):

Human Brain ◽

Information Extraction ◽

Semantic Information ◽

Memory Models ◽

Prior Model ◽

Visual Model ◽

Semantic Models ◽

Simple Detection ◽

Scene Description

Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects. In particular, a great deal of semantic information is carried in the relationships between objects. We have previously shown, that the combination of a visual model and a statistical semantic prior model can improve on the task of mapping images to their associated scene description. In this paper, we review the model and compare it to a novel conditional multi-way model for visual relationship detection, which does not include an explicitly trained visual prior model. We also discuss potential relationships between the proposed methods and memory models of the human brain.

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Automatic Detection and Classification of Road Conditions Using Statistical Model for Autonomous Driving

2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) ◽

10.1109/icspcc50002.2020.9259507 ◽

2020 ◽

Author(s):

Ricky Yuen-Tan Hou

Keyword(s):

Statistical Model ◽

Automatic Detection ◽

Autonomous Driving

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Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211008752 ◽

2021 ◽

Vol 18 (2) ◽

pp. 172988142110087

Author(s):

Qiao Huang ◽

Jinlong Liu

Keyword(s):

Hough Transform ◽

Detection Rate ◽

Real Life ◽

Weather Conditions ◽

Autonomous Driving ◽

Detection Algorithm ◽

Lane Detection ◽

Search Range ◽

Range Restriction ◽

Lighting Conditions

The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.

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Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

Natural Language Engineering ◽

10.1017/s1351324920000352 ◽

2020 ◽

pp. 1-21 ◽

Cited By ~ 2

Author(s):

Clément Dalloux ◽

Vincent Claveau ◽

Natalia Grabar ◽

Lucas Emanuel Silva Oliveira ◽

Claudia Maria Cabral Moro ◽

...

Keyword(s):

Machine Learning ◽

Information Extraction ◽

State Of The Art ◽

Automatic Detection ◽

Brazilian Portuguese ◽

Supervised Machine Learning ◽

Biomedical Domain ◽

Learning Approaches ◽

Cross Domain ◽

Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

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