Lane mark segmentation and identification using statistical criteria on compressed video

Giralt, Juan; Rodríguez-Benítez, Luis; Moreno‐Garcia, Juan; Solana-Cipres, C.; Jiménez, L.

doi:10.3233/ica-130424

Cited by 6 publications

(9 citation statements)

References 27 publications

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“…The first step is based on a technique presented in Ref. that detects lane marks using exclusively information contained in motion vectors. For every frame, what is needed is the information from the slopes corresponding with the left and right lane marks of the lane where the car is travelling.…”

Section: Detection and Representation Of The Road's Shapementioning

confidence: 99%

A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic

Giralt

Moreno‐Garcia

Jiménez-Linares

et al. 2017

Int. J. Intell. Syst.

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This paper presents a system for detecting road departures by comparing linguistic representations for the trajectory of the vehicle with that for the lane marks of the road. All this information is obtained from a single camera processing exclusively the H264 motion vectors extracted from the recorded video. The process of comparison between the linguistic elements allows detecting the subset of continuous frames where there is no logical correspondence between the displacement of the vehicle and the road shape. Since the videos are captured from a moving vehicle, we propose a statistically based process to use domain changing fuzzy sets adapted to traffic scenarios that continuously change. This improves the reliability of the linguistic descriptions that, once compared, are used to detect departures. Lastly, a set of experiments using traffic videos with different characteristics are presented to validate this approach.

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Section: Detection and Representation Of The Road's Shapementioning

confidence: 99%

A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic

Giralt

Moreno‐Garcia

Jiménez-Linares

et al. 2017

Int. J. Intell. Syst.

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“…The problems addressed in this work, namely motion estimation and motion segmentation, play an important role in various vision related tasks (e.g., object tracking [8,33,64], deformation analysis [15,63] and video coding [16,59]). As a backbone for higherlevel scene analysis, there is an even wider spectrum of applications, including gesture recognition [9,22] and scene understanding [61,65].…”

Section: Introductionmentioning

confidence: 99%

Motion estimation and segmentation in depth and intensity videos

Ghuffar

Brosch

Pfeifer

et al. 2014

ICA

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This paper investigates motion estimation and segmentation of independently moving objects in video sequences that contain depth and intensity information, such as videos captured by a Time of Flight camera. Specifically, we present a motion estimation algorithm which is based on integration of depth and intensity data. The resulting motion information is used to derive long-term point trajectories. A segmentation technique groups the trajectories according to their motion and depth similarity into spatio-temporal segments. Quantitative and qualitative analysis of synthetic and real world videos verify the proposed motion estimation and segmentation approach. The proposed framework extracts independently moving objects from videos recorded by a Time of Flight camera.

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“…Each acquisition method has its own advantages and limitations in different application fields. Previous studies indicate that lane marking data captured by visual cameras are widely used for autonomous driving navigation and traffic surveillance [ 2 , 5 , 6 ], based on which numerous efforts have been made to detect, locate, and track lane markings in the spatial domain. However, the study of lane marking detection and location for use in road condition evaluation is neglected.…”

Section: Introductionmentioning

confidence: 99%

“…However, unexpected challenges always appear in lane marking detection and localization due to various interferences such as illumination conditions (occlusion, night time…), camera location and orientation, environmental factors (i.e., foggy days, cloudy and rainy days…), the appearance of the lane markings, the type of road, and so on [ 2 ]. To deal with the abovementioned problems, numerous vision-based lane marking detection and localization algorithms have been proposed, which for structured roads can be roughly grouped into two categories: feature-based methods and model-based techniques [ 6 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

Luo

Wang

2018

Sensors

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Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification.

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Lane mark segmentation and identification using statistical criteria on compressed video

Cited by 6 publications

References 27 publications

A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic

A Road Departure Warning System Based on Video Motion Analysis and Fuzzy Logic

Motion estimation and segmentation in depth and intensity videos

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

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