Adaptive lane finding in road traffic image analysis

Stewart, Brian; Reading, Ivan; Thomson, Mitchell G. A.; Binnie, T.D.; Dickinson, K W; Wan, Chenxia

doi:10.1049/cp:19940441

Cited by 31 publications

(16 citation statements)

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“…Stewart et al present an automatic lane-finding algorithm based on the activity map for traffic monitoring (14). The activity map is generated by detecting a significant scene change.…”

Section: Xueyan Du and Mark Hickmanmentioning

confidence: 99%

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Estimation of a Road Mask to Improve Vehicle Detection and Tracking in Airborne Imagery

Hickman

2012

Transportation Research Record: Journal of the Transportation R

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on roadways. Screening out nonroad areas during image processing is desirable for two reasons: first, vehicle detection and tracking will only be performed within roadway areas, and, therefore, there will be no false detections of vehicle movements in these nonroad areas; second, the computational effort can be reduced because only roadway areas are processed. The term "road mask" describes a screen applied to an image to eliminate irrelevant areas of the image and to focus the computational effort on the roadway itself.As part of the National Consortium on Remote Sensing in Transportation-Flows, a software tool called "tracking and registration of airborne video image sequences" (TRAVIS) has been developed to extract vehicle positions from airborne imagery to assist in the analysis of microscopic traffic behaviors (1, 2). The input into TRAVIS is a sequence of images from the video. TRAVIS registers the image sequences to an initial common reference frame, detects the vehicles in the images, and tracks the vehicles through the image sequence. The output of TRAVIS is a sequence of vehicle pixel coordinates as they are tracked through the image sequence (3). The research here describes a method to generate a road mask from basic vehicle movements in airborne imagery, with the ultimate goal of improving the detection and tracking of vehicles. This is part of a continuing effort to enhance the software. Literature reviewMany algorithms have been developed to extract roadway boundaries for various purposes. Most of them fall into one of three categories: the Hough transformation-based approaches (4, 5), the deformable template-based approaches (6-9), and the line segment groupingbased approaches (10). For example, Bandera et al. use a mean shift-based clustering in the Hough domain to detect line segments in edge images (4). A line segment random window randomized Hough transformation is used to find favorable line segments. Then, a variable bandwidth mean shift algorithm is applied to cluster items in the Hough parameter space. The performance of the algorithm is good in terms of the computation time and the ability to detect line segments. However, there are a set of parameters to calibrate; for example, the minimal line segment length and the minimal gap between two line segments.In contrast, Kluge and Lakshmanan describe a likelihood of image shape algorithm for lane detection based on a deformable template approach, which uses intensity gradient information (6). The lane detection problem is formulated as finding a set of the most likely lane edge parameters. A shape model defines a set of shape parameters, and a likelihood function measures how well a given Estimation of a Road Mask to Improve Vehicle Detection and Tracking in Airborne Imagery Xueyan Du and Mark HickmanThis research proposes a method to estimate a road mask in airborne imagery to improve vehicle detection and tracking. The road mask can remove false detections and reduce computation time. The road mask was estimated on the basis of smoothed (i...

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“…Stewart et al present an automatic lane-finding algorithm based on the activity map for traffic monitoring (14). The activity map is generated by detecting a significant scene change.…”

Section: Xueyan Du and Mark Hickmanmentioning

confidence: 99%

“…The useful information of the vehicles on the roadway has seldom been taken into consideration, except in Stewart et al and Melo et al (14,15). Because vehicles are running on the roadway, the positions of the vehicles can potentially be used to infer the roadway boundaries.…”

Section: Xueyan Du and Mark Hickmanmentioning

confidence: 99%

Estimation of a Road Mask to Improve Vehicle Detection and Tracking in Airborne Imagery

Hickman

2012

Transportation Research Record: Journal of the Transportation R

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“…To carry out some of these tasks it is needed to delimit the road and lane boundaries. This can be automatically achieved by applying the technique exposed in [10].…”

Section: T R a F F I C M O N I T O R I N G F A C I L I T I E Smentioning

confidence: 99%

Applying computer vision techniques to traffic monitoring tasks

Badenas

Pla

1998

Lecture Notes in Computer Science

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A b s t r a c t . This paper presents a method I for tracking and segmenting vehicles in a traffic scene. The approach is based on a frame to frame segmentation followed by a tracking process. As opposed to usual segmentation methods, our method feedbacks segmentation with tracking information for improving results. Several facilities are provided for traffic monitoring such as vehicles trajectories surveillance, segmentation of vehicle shape, measuring the mean velocity of the traffic, counting the vehicles that are moving on the lanes of a road or a motorway, counting the vehicles that stop at a junction and detection of events such as a vehicle stops on a road or a possible accident.Key Words : Region tracking, motion analysis, motion segmentation, traffic monitoring, vehicle surveillance I n t r o d u c t i o nIn recent years, as result of advances in information techniques both in terms of computational power and cost, it has become possible to use computer vision to perform many everyday tasks. This paper shows how it can be applied to traffic monitoring substituting existing methods such as Inductive Loop Detectors and Microwave Vehicle Detectors which are more expensive, and have more limited usefulness.A typical traffic scene consists of untextured objects with a regular shape moving over a surface which is also untextured. In this context, a traffic monitoring system has to survey moving objects detecting anomalous behaviors and situations, and producing measures.Several works have been reported on the application of computer vision to traffic monitoring. One of the early works [1] describes a system with motion analysis based on simple frame differencing. This simple approach can not provide enough information and produces poor results due to it is very sensitive to noise. Dubuisson and Jain [2] proposed a technique for segmenting vehicles also based on image substraction, but combined with color segmentation of regions. [5], where the edges extracted from the image are matched to 3-D segments of a generic car model that is projected onto the image plane. This matching is only applied to edges moving with coherent motion, to avoid mixing up edges from different vehicles. 2-D models have a lower computational cost, but present problems when the size and the number of vehicles increase. In [7], B-splines with four control points are fitted to vehicles projection. Weber at al.[6] method describe the contours by a closed cubic spline and employs two Kalman filters, one for estimating the affine motion parameters and other one for estimating the shape of the contours.A different group of methods for motion analysis tracks single tokens such as points or lines, that are extracted fl'om the image,[8], [9]. These methods present problems due to they do not provide explicit grouping of tokens moving with coherent motion and are quite sensitive to noise.We propose in this paper an approach based on tracking regions which integrates information recorded over a sequence on a frame by frame basis for improving se...

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“…Several techniques have been proposed for road detection in traffic cameras. Broadly speaking these can be divided into three categories: (1) activity-driven [3], [4], (2) feature-driven [5], [6], [7] and (3) model-driven [8], [9]. The activity-driven approaches benefit from the relatively high vehicle motion activity along the roads.…”

Section: Introductionmentioning

confidence: 99%

Road Boundary Detection in Challenging Scenarios

Helala

Qureshi

2012

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance

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Abstract-This paper presents a new approach for automatic road detection in traffic cameras. The technique proposed here detects the dominant road boundary and estimates the vanishing point in images captured by traffic cameras under a wide range of lighting and environmental conditions, e.g., in images of unlit highways captured at night, etc. The approach starts by segmenting the traffic scene into a number of superpixel regions. The contours of these regions are used to generate a large number of edges which are organized into clusters of co-linearly similar sets using hierarchical bottom up clustering. A confidence level is assigned to each cluster using a statistical approach and the best clusters are chosen. Pairs of clusters with high confidence levels are then ranked and filtered according to image perspective and activity. The top ranked pair is selected as the road boundary. The proposed technique is tested on a real world dataset collected from the Ontario 401 traffic surveillance system. Experimental results demonstrates a distinct speedup and improvement in accuracy of the proposed technique in detecting the dominant road boundary in challenging scenarios compared to the state of the art Gabor filter based technique.

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Adaptive lane finding in road traffic image analysis

Cited by 31 publications

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Estimation of a Road Mask to Improve Vehicle Detection and Tracking in Airborne Imagery

Estimation of a Road Mask to Improve Vehicle Detection and Tracking in Airborne Imagery

Applying computer vision techniques to traffic monitoring tasks

Road Boundary Detection in Challenging Scenarios

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