An Improved Lane Detection and Tracking Method for Lane Departure Warning Systems

Hammami, Mohamed; Romdhane, Nadra Ben; Ben-Abdallah, Hanêne

doi:10.4018/ijcvip.2013070101

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…It is usually set at the lower part of the image. It can be predetermined either manually during the setup of the surveillance camera or automatically by using lane detection algorithms [19][20][21][22][23]. Then, the detection is only performed in the ROI.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multi Objects Tracking in Nighttime Traffic Scenes

Taha

Zayed

Nazmy³

et al. 2015

The 7th International Conference on Information Technology

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As road networks become more congested, traffic surveillance using computer vision techniques is increasingly important. Traffic surveillance can help in improving road network efficiency, rerouting traffic when accidents occur and minimizing delays. Although, there are many algorithms developed to detect and track moving vehicles in daytime, only a handful of techniques have been proposed for nighttime traffic scenes. In the night environment, the moving vehicles are commonly identified by detecting and locating vehicle headlights and taillights. This paper proposes an effective method for detecting and tracking moving vehicles in nighttime. The proposed method identifies vehicles by detecting and locating vehicle lights using automatic thresholding and connected components extraction. Detected lamps are then paired using rule based component analysis approach and tracked using Kalman Filter (KF). The automatic thresholding approach provides a robust and adaptable detection process that operates well under various nighttime illumination conditions. Furthermore, most nighttime tracking algorithms detects vehicles by locating either headlights or rear lights. However, the proposed method has the ability to track vehicles through detecting vehicle headlights and/or rear lights. Several experiments are presented that demonstrate the feasibility and the effectiveness of the proposed method to detect and track vehicles in various nighttime environments.

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Section: Proposed Methodsmentioning

confidence: 99%

Multi Objects Tracking in Nighttime Traffic Scenes

Taha

Zayed

Nazmy³

et al. 2015

The 7th International Conference on Information Technology

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“…The authors of [127] and [128] implemented K-NN for an LDW ADAS application. For ANN, [129] and [130] are two prestigious works on LDW.…”

Section: E State-of-the-art Machine Learning Algorithms For Adas Appl...mentioning

confidence: 99%

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar

Hong

Moore

2023

IEEE Open J. Veh. Technol.

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Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

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Intelligent Fusion of Infrared and Visible Spectrum for Video Surveillance Application

Boukhriss

Fendri

Hammami

2015

Artificial Intelligence and Soft Computing

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Abstract. In video surveillance, we can rely on either a visible spectrum or an infrared one. In order to profit from both of them, several fusion methods were proposed in literature: low-level fusion, middle-level fusion and high-level fusion. The first one is the most used for moving objects' detection. It consists in merging information from visible image and infrared one into a new synthetic image to detect objects. However, the fusion process may not preserve all relevant information. In addition, perfect correlation between the two spectrums is needed. In This paper, we propose an intelligent fusion method for moving object detection. The proposed method relies on one of the two given spectrum at once according to weather conditions (darkness, sunny days, fog, snow, etc.). Thus, we first extract a set of low-level features (visibility, local contrast, sharpness, hue, saturation and value), then a prediction model is generated by supervised learning techniques. The classification results on 15 sequences with different weather conditions indicate the effectiveness of the extracted features, by using C4.5 as classifier.

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An Improved Lane Detection and Tracking Method for Lane Departure Warning Systems

Cited by 5 publications

References 16 publications

Multi Objects Tracking in Nighttime Traffic Scenes

Multi Objects Tracking in Nighttime Traffic Scenes

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Intelligent Fusion of Infrared and Visible Spectrum for Video Surveillance Application

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