A stereo-based vehicle detection method under windy conditions

Suwa, Masaki; Wu, Ying; Kobayashi, Minoru; Kimachi, Masatoshi; Ogata, Satoshi

doi:10.1109/ivs.2000.898349

Cited by 12 publications

(11 citation statements)

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“…Therefore, for the HSV space, when the three components of RGB equal, Hue is not defined. So in these color-invariant colorful space, this system uses C1C2C3 [6] model, with its color space as Formula (3)(4) shown: …”

Section: Real-time Segmentation Based On Elimination Of the Shadowmentioning

confidence: 99%

“…Compared with traditional methods, it bases on video image treatment and visual technology, with many advantages of speediness, low-cost, non-damage of road surface, installation without interrupting the traffic, wide testing coverage, many detection parameters, easy maintenance, wide range of application and capability to provide real traffic scenes, and so forth [ 2,3] . Therefore, the method based on video processing and vision techniques [4] becomes increasingly widely used in the traffic monitoring system.…”

Section: Introductionmentioning

confidence: 99%

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Research on Intelligent Transportation Vehicle Detection and Tracking Algorithms Based on Video

Zhu

Kong

2009

2009 2nd International Congress on Image and Signal Processing

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By means of Sequence Mean Method based on a series of consecutive video images to extract the background, this paper makes use of difference model of colored images to make difference on both images, hence to obtain effective goals of moving vehicles. As regard to post-treatment of the test results, it proposes an algorithm for real-time segmentation and integration of the moving target. The real-time segmentation mainly adopts an improved algorithm based on the elimination of the shadow, to divide the moving target and by matching the template, separate the overlapping vehicles. In the time of integration, this essay brings forth a type of interest function to make a real-time filling of the internal hollowness of the moving target, and have a realtime merger by making use of the distances between the rectangles. Experimental results have shown that the various proposed algorithm is characterized by simplicity, easy-to-use and high real-time. The vehicle detection rate is over 96%.

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Section: Real-time Segmentation Based On Elimination Of the Shadowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Transportation Vehicle Detection and Tracking Algorithms Based on Video

Zhu

Kong

2009

2009 2nd International Congress on Image and Signal Processing

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“…Knoeppel et al [14] developed a stereo-system detecting vehicles up to 150 m. The main problem with stereo-based methods is that they are sensitive to the recovered camera parameters. Accurate and robust methods are required to recover these parameters because of vehicle vibrations due to vehicle motion or windy conditions [15].…”

Section: A Two-step Schemementioning

confidence: 99%

Monocular precrash vehicle detection: features and classifiers

Sun

Bebis

Miller

2006

IEEE Trans. on Image Process.

223

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Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

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“…The vehicle detection results will be heavily limited by the detecting results of the lane boundaries. The stereo technique is used to estimate the depth information for detecting vehicles in [2,19,20]. The vehicle can be detected robust without using lane information in these approaches.…”

Section: Introductionmentioning

confidence: 99%

Driver Assistance System Using Integrated Information from Lane Geometry and Vehicle Direction

Huang

Chan

et al. 2007

2007 IEEE Intelligent Transportation Systems Conference

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This paper presents an approach to detect multiple lane and vehicles. Instead of assuming that the processes of lane and vehicle detection are independently, we integrate these two processes in a mutually supporting way to achieve more accurate results. In lane boundary detection, the features of lane boundary often affect by the edge and color of the vehicle. Furthermore, the results of vehicle detection could be non-robust if there are some non-vehicle objects that have similar features to vehicle. Here, we use the distance of the position between central position of lane boundary and vehicle position from hypotheses to filter out the non-vehicle object.And we use the similarity of the lane boundaries direction and the moving direction from hypotheses to get the optimal lane solution. By applying iterative optimization algorithm, we can achieve sub-optimal solution of lane and vehicle detection and the experimental results shows that the error rate is successfully reduced from 32.6% to 2.7%.

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A stereo-based vehicle detection method under windy conditions

Cited by 12 publications

References 0 publications

Research on Intelligent Transportation Vehicle Detection and Tracking Algorithms Based on Video

Research on Intelligent Transportation Vehicle Detection and Tracking Algorithms Based on Video

Monocular precrash vehicle detection: features and classifiers

Driver Assistance System Using Integrated Information from Lane Geometry and Vehicle Direction

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