The Experts below are selected from a list of 27408 Experts worldwide ranked by ideXlab platform
Karl D. Iagnemma - One of the best experts on this subject based on the ideXlab platform.
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Road Detection using support vector machine based on online learning and evaluation
Intelligent Vehicles Symposium (IV) 2010 IEEE, 2010Co-Authors: Zhou Shengyan, Gong Jianwei, Chen Huiyan, Xiong Guangming, Karl D. IagnemmaAbstract:Road Detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view Road Detection. Specifically, we propose using Support Vector Machines (SVM) for Road Detection and effective approach for self-supervised online learning. The proposed Road Detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying Road and non-Road classes and improves the adaptability of the Road Detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based Road Detection algorithm on intelligent vehicle.
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Intelligent Vehicles Symposium - Road Detection using support vector machine based on online learning and evaluation
2010 IEEE Intelligent Vehicles Symposium, 2010Co-Authors: Shengyan Zhou, Jianwei Gong, Guangming Xiong, Huiyan Chen, Karl D. IagnemmaAbstract:Road Detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view Road Detection. Specifically, we propose using Support Vector Machines (SVM) for Road Detection and effective approach for self-supervised online learning. The proposed Road Detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying Road and non-Road classes and improves the adaptability of the Road Detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based Road Detection algorithm on intelligent vehicle.
Antonio M. López - One of the best experts on this subject based on the ideXlab platform.
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Road Detection via On--line Label Transfer
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: José M. Alvarez, Ferran Diego, Joan Serrat, Antonio M. LópezAbstract:Vision-based Road Detection is an essential functionality for supporting advanced driver assistance systems (ADAS) such as Road following and vehicle and pedestrian Detection. The major challenges of Road Detection are dealing with shadows and lighting variations and the presence of other objects in the scene. Current Road Detection algorithms characterize Road areas at pixel level and group pixels accordingly. However, these algorithms fail in presence of strong shadows and lighting variations. Therefore, we propose a Road Detection algorithm based on video alignment. The key idea of the algorithm is to exploit the similarities occurred when a vehicle follows the same trajectory more than once. In this way, Road areas are learned in a first ride and then, this Road knowledge is used to infer areas depicting drivable Road surfaces in subsequent rides. Two different experiments are conducted to validate the proposal on different video sequences taken at different scenarios and different daytime. The former aims to perform on-line Road Detection. The latter aims to perform off-line Road Detection and is applied to automatically generate the ground-truth necessary to validate Road Detection algorithms. Qualitative and quantitative evaluations prove that the proposed algorithm is a valid Road Detection approach.
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Combining Priors, Appearance, and Context for Road Detection
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: José M. Alvarez, Antonio M. López, Theo Gevers, Felipe LumbrerasAbstract:Detecting the free Road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based Road Detection methods are usually based solely on low-level features. Furthermore, they generally assume structured Roads, Road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, Road priors and contextual information are introduced for Road Detection. First, we propose an algorithm to estimate Road priors online using geographical information, providing relevant initial information about the Road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and Road geometry, are used in addition to low-level cues derived from the appearance of Roads. Finally, a generative model is used to combine these cues and priors, leading to a Road Detection method that is, to a large degree, robust to varying imaging conditions, Road types, and scenarios.
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Road Detection based on illuminant invariance
IEEE Transactions on Intelligent Transportation Systems, 2011Co-Authors: José M. Alvarez, Antonio M. LópezAbstract:By using an onboard camera, it is possible to detect the free Road surface ahead of the ego-vehicle. Road Detection is of high relevance for autonomous driving, Road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian Detection. The key for vision-based Road Detection is the ability to classify image pixels as belonging or not to the Road surface. Identifying Road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the Road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based Road Detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either Road shape or temporal restrictions. Quantitative and qualitative experiments on real-world Road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue–saturation–intensity (HSI)-based algorithms.
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3d scene priors for Road Detection
Computer Vision and Pattern Recognition, 2010Co-Authors: José M. Alvarez, Theo Gevers, Antonio M. LópezAbstract:Vision–based Road Detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing Detection. However, current vision–based Road Detection methods are usually based on low–level features and they assume structured Roads, Road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low–level cues. Low–level photometric invariant cues are derived from the appearance of Roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D Road stages. Moreover, temporal Road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify Road sequences. Large scale experiments on Road sequences show that the Road Detection method is robust to varying imaging conditions, Road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest Road Detection accuracy when compared to state–of–the–art methods.
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CVPR - 3D Scene priors for Road Detection
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010Co-Authors: José M. Alvarez, Theo Gevers, Antonio M. LópezAbstract:Vision–based Road Detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing Detection. However, current vision–based Road Detection methods are usually based on low–level features and they assume structured Roads, Road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low–level cues. Low–level photometric invariant cues are derived from the appearance of Roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D Road stages. Moreover, temporal Road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify Road sequences. Large scale experiments on Road sequences show that the Road Detection method is robust to varying imaging conditions, Road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest Road Detection accuracy when compared to state–of–the–art methods.
José M. Alvarez - One of the best experts on this subject based on the ideXlab platform.
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3-D LiDAR + Monocular Camera: An Inverse-Depth-Induced Fusion Framework for Urban Road Detection
IEEE Transactions on Intelligent Vehicles, 2018Co-Authors: Yigong Zhang, José M. Alvarez, Jian Yang, Hui KongAbstract:Road Detection is an important task in autonomous navigation systems. In this paper, we propose a Road Detection framework induced by the inverse depth of LiDAR's point cloud. This framework is a fusion of a 3-D LiDAR and a monocular camera, where the 3-D point cloud of LiDAR is projected onto the camera's image frame, to exploit both range and color information. For the same Road Detection task, we propose an inverse-depth aware fully convolutional neural network based on image information and a line scanning strategy based on an inverse-depth histogram of LiDAR's point cloud. Finally, a conditional random field fusion method integrates the two Road Detection results. Our method is evaluated on KITTI-Road benchmark. Experiments demonstrate that our method achieves the state-of-the-art performance in Road Detection among all the referable methods that have ever reported their results on the KITTI-Road benchmark.
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Road Detection via On--line Label Transfer
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: José M. Alvarez, Ferran Diego, Joan Serrat, Antonio M. LópezAbstract:Vision-based Road Detection is an essential functionality for supporting advanced driver assistance systems (ADAS) such as Road following and vehicle and pedestrian Detection. The major challenges of Road Detection are dealing with shadows and lighting variations and the presence of other objects in the scene. Current Road Detection algorithms characterize Road areas at pixel level and group pixels accordingly. However, these algorithms fail in presence of strong shadows and lighting variations. Therefore, we propose a Road Detection algorithm based on video alignment. The key idea of the algorithm is to exploit the similarities occurred when a vehicle follows the same trajectory more than once. In this way, Road areas are learned in a first ride and then, this Road knowledge is used to infer areas depicting drivable Road surfaces in subsequent rides. Two different experiments are conducted to validate the proposal on different video sequences taken at different scenarios and different daytime. The former aims to perform on-line Road Detection. The latter aims to perform off-line Road Detection and is applied to automatically generate the ground-truth necessary to validate Road Detection algorithms. Qualitative and quantitative evaluations prove that the proposed algorithm is a valid Road Detection approach.
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WACV - Data-driven Road Detection
IEEE Winter Conference on Applications of Computer Vision, 2014Co-Authors: José M. Alvarez, Mathieu Salzmann, Nick BarnesAbstract:In this paper, we tackle the problem of Road Detection from RGB images. In particular, we follow a data-driven approach to segmenting the Road pixels in an image. To this end, we introduce two Road Detection methods: A top-down approach that builds an image-level Road prior based on the traffic pattern observed in an input image, and a bottom-up technique that estimates the probability that an image superpixel belongs to the Road surface in a nonparametric manner. Both our algorithms work on the principle of label transfer in the sense that the Road prior is directly constructed from the ground-truth segmentations of training images. Our experimental evaluation on four different datasets shows that this approach outperforms existing top-down and bottom-up techniques, and is key to the robustness of Road Detection algorithms to the dataset bias.
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Combining Priors, Appearance, and Context for Road Detection
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: José M. Alvarez, Antonio M. López, Theo Gevers, Felipe LumbrerasAbstract:Detecting the free Road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based Road Detection methods are usually based solely on low-level features. Furthermore, they generally assume structured Roads, Road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, Road priors and contextual information are introduced for Road Detection. First, we propose an algorithm to estimate Road priors online using geographical information, providing relevant initial information about the Road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and Road geometry, are used in addition to low-level cues derived from the appearance of Roads. Finally, a generative model is used to combine these cues and priors, leading to a Road Detection method that is, to a large degree, robust to varying imaging conditions, Road types, and scenarios.
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Road Detection based on illuminant invariance
IEEE Transactions on Intelligent Transportation Systems, 2011Co-Authors: José M. Alvarez, Antonio M. LópezAbstract:By using an onboard camera, it is possible to detect the free Road surface ahead of the ego-vehicle. Road Detection is of high relevance for autonomous driving, Road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian Detection. The key for vision-based Road Detection is the ability to classify image pixels as belonging or not to the Road surface. Identifying Road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the Road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based Road Detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either Road shape or temporal restrictions. Quantitative and qualitative experiments on real-world Road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue–saturation–intensity (HSI)-based algorithms.
Zhou Shengyan - One of the best experts on this subject based on the ideXlab platform.
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Road Detection using support vector machine based on online learning and evaluation
Intelligent Vehicles Symposium (IV) 2010 IEEE, 2010Co-Authors: Zhou Shengyan, Gong Jianwei, Chen Huiyan, Xiong Guangming, Karl D. IagnemmaAbstract:Road Detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view Road Detection. Specifically, we propose using Support Vector Machines (SVM) for Road Detection and effective approach for self-supervised online learning. The proposed Road Detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying Road and non-Road classes and improves the adaptability of the Road Detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based Road Detection algorithm on intelligent vehicle.
Shengyan Zhou - One of the best experts on this subject based on the ideXlab platform.
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Intelligent Vehicles Symposium - Road Detection using support vector machine based on online learning and evaluation
2010 IEEE Intelligent Vehicles Symposium, 2010Co-Authors: Shengyan Zhou, Jianwei Gong, Guangming Xiong, Huiyan Chen, Karl D. IagnemmaAbstract:Road Detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view Road Detection. Specifically, we propose using Support Vector Machines (SVM) for Road Detection and effective approach for self-supervised online learning. The proposed Road Detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying Road and non-Road classes and improves the adaptability of the Road Detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based Road Detection algorithm on intelligent vehicle.
