Driving assistance system based on the detection of head-on collisions

Martínez, Elisa; Díaz, Matías; Melenchón, Javier; Montero, José Antonio; Iriondo, Ignasi; Socoró, Joan Claudi

doi:10.1109/ivs.2008.4621275

Cited by 19 publications

(15 citation statements)

References 3 publications

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“…Optical flow [90], a fundamental machine vision tool, has been used for monocular vehicle detection [91]. In [27], a combination of optical flow and symmetry tracking was used for vehicle detection.…”

Section: ) Motion-based Approachesmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

791

313

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: ) Motion-based Approachesmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

791

313

View full text Add to dashboard Cite

show abstract

“…In [116], optical flow was proposed to detect any type of frontal collision. In [117], the optical flow method was applied to a scene descriptor for classifying urban traffic.…”

Section: A Vehicle Detectionmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

280

114

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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“…In [2], [22], adaptive background modelling was used, with vehicles detected based on motion that differentiated them from the background. Optical flow [23], a fundamental machine vision tool, has been used for monocular vehicle detection [24]. In [25], a combination of optical flow and symmetry tracking was used for vehicle detection.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for On-road Vehicle Detection and Tracking

Jaafari¹,

Ansari²,

Koutti³

et al. 2016

ijacsa

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Abstract-On the basis of a necessary development of the road safety, vision-based vehicle detection techniques have gained an important amount of attention. This work presents a novel vehicle detection and tracking approach, and structured based on a vehicle detection process starting from, images or video data acquired from sensors installed on board of the vehicle, to vehicle detection and tracking. The features of the vehicle are extracted by the proposed GIST image processing algorithm, and recognized by the state-of-art Support Vectors Machine classifier. The tracking process was performed based on edge features matching approach. The Kalman filter was used to correct the measurements. Extensive experiments were carried out on real image data validate that it is promising to employ the proposed approach for on road vehicle detection and tracking.

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Driving assistance system based on the detection of head-on collisions

Cited by 19 publications

References 3 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Overview of Environment Perception for Intelligent Vehicles

A Novel Approach for On-road Vehicle Detection and Tracking

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