Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection

Σιόγκας, Γεώργιος; Dermatas, Evangelos

doi:10.1109/tits.2012.2223686

Cited by 23 publications

(13 citation statements)

References 33 publications

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“…The third type of road detection works use ego-vehicles with onboard cameras with driver assistance systems or UGVs autonomous navigation systems. A substantial amount of work [7], [25]- [29] have been done in this area. Since the focus of this paper is on road detection and tracking using low-/mid- altitude UAVs, we only give a review of the most related works in this area.…”

Section: IImentioning

confidence: 99%

“…In the previous works of road detection and tracking, most approaches use the color (texture) and/or structure (geometry) properties of roads. Among them, the combination of road color and boundary information have achieved more robust and ac-curate results than using only one of them in road detection, as shown in the work [6], [7]. Therefore, we are paying a note of using both types of information.…”

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confidence: 99%

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Tracking and Map Formatting of Road Using Unmanned Ground Vehicle

Kumar¹

2017

IJRASET

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An unmanned ground vehicle (UGV) has many applications in a variety of fields. Detection and tracking of a specific road in UGV videos play an important role in automatic UGV navigation, traffic monitoring, and is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UGV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based tracking scheme is developed to automatically tracking road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when needed and most work in locating the road is rapidly done via very fast homography-based tracking. They use UGVs in autonomous navigation to follow roads/rivers, oil-gas pipeline inspection, and traffic parameters measurements. UAVs equipped with cameras are viewed as a low-cost platform that can provide efficient data acquisition methods for intelligent transport systems. With the increasing vehicles usage and their traffic management demands. this kind of platform becomes attractively popular. Conventional traffic data collection [5] relying on fixed infrastructure is only limited to a local region and, thus, it is expensive and labor intensive to monitor traffic activities across broad areas. In comparison, UAV has advantages, including: There is a low cost to monitor over long distances; it is flexible for flying across broad spatial and temporal scales; and it is capable of carrying various types of sensors to collect abundant data. Detection in deep about road areas can provide users the regions of interest for further navigation, detection and data collection procedures, benefiting their efficiencies and accuracies. In the previous works of road detection and tracking, most approaches use the color (texture) and/or structure (geometry) properties of roads. Among them, the combination of road color and boundary information have achieved more robust and ac-curate results than using only one of them in road detection, as shown in the work [6], [7]. Therefore, we are paying a note of using both types of information. Because real time is required in many UAV-based applications, our major target is how to effectively combine both types of information for road detection/ tracking in an efficient way. There are two rules that make one integrated framework efficient. First, each and every component of the framework should be fast. Second, if one component is faster than the others in achieving the same purpose, fastest component should be prioritized as much as possible. we follow the above mentioned two rules to make the framework fast. our framework includes two components: road detection, road tracking. In road detection, we utilize the GraphCut algorithm [8] considering its efficiency and powerful segmentation performance in 2-D color images. In road tr...

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Section: IImentioning

confidence: 99%

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confidence: 99%

Tracking and Map Formatting of Road Using Unmanned Ground Vehicle

Kumar¹

2017

IJRASET

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“…By defining a certain number T of absorption thresholds t i , the absorption distances AD α (t i ), i.e., the locations where the integral value reaches a certain threshold t i , are obtained as SPRAY features (8). The graph in Fig.…”

Section: A Individual Spray Featuresmentioning

confidence: 99%

“…detectable due to, e.g., parking cars on the side occluding them, then current systems based on delimiter detection [4]- [7] are not working. Furthermore, also the appearance of the road itself, i.e., the color and texture of the asphalt, is strongly varying and makes appearance-based road segmentation [8]- [10] highly challenging. Most importantly, the separation of the road area into different semantic categories (e.g., ego-lane versus non-ego-lane) is not straightforward using segmentation-based approaches because the visual appearance of the lanes on the local level is rather identical.…”

Section: Introductionmentioning

confidence: 99%

Monocular Road Terrain Detection by Combining Visual and Spatial Information

Fritsch

Kühnl

Kummert

2014

IEEE Trans. Intell. Transport. Syst.

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For future driver assistance systems and autonomous vehicles, the road course, i.e., the width and shape of the driving path, is an important source of information. In this paper, we introduce a new hierarchical two-stage approach for learning the spatial layout of road scenes. In the first stage, base classifiers analyze the local visual properties of patches extracted from monocular camera images and provide metric confidence maps. We use classifiers for road appearance, boundary appearance, and lane-marking appearance. The core of the proposed approach is the computation of SPatial RAY (SPRAY) features from each metric confidence map in the second stage. A boosting classifier selecting discriminative SPRAY features can be trained for different types of road terrain and allows capturing the local visual properties together with their spatial layout in the scene. In this paper, the extraction of road area and ego-lane on inner-city video streams is demonstrated. In particular, the detection of the ego-lane is a challenging semantic segmentation task showing the power of SPRAY features, because on a local appearance level, the ego-lane is not distinguishable from other lanes. We have evaluated our approach operating at 20 Hz on a graphics processing unit on a publicly available data set, demonstrating the performance on a variety of road types and weather conditions.

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“…The performance of lane detection based on camera sensors may vary according to the weather and environments. Therefore, in the past few years, various approaches for lane detection were proposed and their robustness to various environments was successfully demonstrated [4][5][6][7][8][9][10][11][12]. Although the previous approaches based on the camera image processing are effective at detecting a lane, the performances of those methods depend on the performance of the camera sensor.…”

Section: Introductionmentioning

confidence: 99%

Predictive virtual lane method using relative motions between a vehicle and lanes

Son

Kim

Lee

et al. 2014

Int. J. Control Autom. Syst.

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We propose a new approach for virtual lane prediction. The main contribution of the proposed method is that the predicted virtual lane can be substituted for lane detection using a camera sensor when the camera image processing fails to detect the lane. The proposed method generates the predicted virtual lane using the relative movement between a vehicle and a lane. To predict the lane, a third-order polynomial function of the longitudinal distance is used as a lane model. Each coefficient of the lane polynomial function at the next sampling time is geometrically calculated using the relative movement of a vehicle, the lanes, the longitudinal velocity and the yaw of the vehicle at the present time. Then, the predictive virtual lane at the next sampling time is obtained without the lane information from the camera sensor at the next sampling time. The proposed method is simple enough that it is suitable for real implementation. The performance of the proposed method was evaluated via experiments with a test vehicle.

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Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection

Cited by 23 publications

References 33 publications

Tracking and Map Formatting of Road Using Unmanned Ground Vehicle

Tracking and Map Formatting of Road Using Unmanned Ground Vehicle

Monocular Road Terrain Detection by Combining Visual and Spatial Information

Predictive virtual lane method using relative motions between a vehicle and lanes

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