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Author |
Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez |
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Title |
Road Scene Segmentation from a Single Image |
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Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
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Volume |
7578 |
Issue |
VII |
Pages |
376-389 |
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Keywords  |
road detection |
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Abstract |
Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined |
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Florence, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-33785-7 |
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ECCV |
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ADAS;ISE |
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no |
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Admin @ si @ AGL2012; ADAS @ adas @ agl2012a |
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2022 |
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Author |
Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez |
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Title |
Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
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Volume |
7584 |
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586-595 |
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Keywords |
road detection |
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Abstract |
Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo. |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-33867-0 |
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ECCVW |
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ADAS;ISE |
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Admin @ si @ ALG2012; ADAS @ adas |
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2187 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias |
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Title |
Scene Representations for Autonomous Driving: an approach based on polygonal primitives |
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Conference Article |
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Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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503-515 |
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Keywords |
Scene reconstruction; Point cloud; Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Lisboa; Portugal; November 2015 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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no |
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Call Number |
Admin @ si @ OSS2015a |
Serial |
2662 |
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Author |
Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers |
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Title |
Improving HOG with Image Segmentation: Application to Human Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
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Volume |
7517 |
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Pages |
178-189 |
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Keywords |
Segmentation; Pedestrian Detection |
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Abstract |
In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function. |
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Brno, Czech Republic |
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Springer Berlin Heidelberg |
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Editor |
J. Blanc-Talon et al. |
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English |
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LNCS |
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0302-9743 |
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978-3-642-33139-8 |
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ACIVS |
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ADAS;ISE |
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no |
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Call Number |
ADAS @ adas @ SLV2012 |
Serial |
1980 |
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Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
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Title |
On-line Semantic Perception Using Uncertainty |
Type |
Conference Article |
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Year |
2012 |
Publication |
International Conference on Intelligent Robots and Systems |
Abbreviated Journal |
IROS |
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Pages |
4185-4191 |
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Keywords |
Semantic Segmentation |
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Abstract |
Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance |
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IROS |
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ADAS |
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ADAS @ adas @ NRR2012 |
Serial |
2378 |
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Author |
Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
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Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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Pages |
2312 - 2319 |
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Keywords |
Semantic Segmentation |
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Abstract |
Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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ADAS; 600.057 |
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no |
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Call Number |
ADAS @ adas @ RBN2013 |
Serial |
2377 |
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Author |
Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio |
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Title |
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation |
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Conference Article |
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2017 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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Keywords |
Semantic Segmentation |
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State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
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Honolulu; USA; July 2017 |
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CVPRW |
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MILAB; ADAS; 600.076; 600.085; 601.281 |
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ADAS @ adas @ JDV2016 |
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2866 |
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Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
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Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
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Conference Article |
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2016 |
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5th International Conference Games and Learning Alliance |
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10056 |
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50-59 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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GALA |
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ADAS;IAM; |
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HAC2016 |
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2864 |
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Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
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Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
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Conference Article |
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2016 |
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14th European Conference on Computer Vision Workshops |
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9915 |
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894-900 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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ADAS;IAM; 600.085; 600.076 |
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MHE2016 |
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2865 |
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German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios |
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Conference Article |
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2013 |
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24th British Machine Vision Conference |
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SLAM |
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Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach. |
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Bristol; UK; September 2013 |
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BMVC |
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ADAS |
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Admin @ si @ RGS2013b; ADAS @ adas @ |
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2274 |
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