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Fadi Dornaika; Jose Manuel Alvarez; Angel Sappa; Antonio Lopez |
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Title |
A New Framework for Stereo Sensor Pose through Road Segmentation and Registration |
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Year |
2011 |
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IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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12 |
Issue |
4 |
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954-966 |
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Keywords |
road detection |
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Abstract |
This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework. |
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1524-9050 |
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ADAS |
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Admin @ si @ DAS2011; ADAS @ adas @ das2011a |
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1833 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Implicit Polynomial Representation through a Fast Fitting Error Estimation |
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Year |
2012 |
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IEEE Transactions on Image Processing |
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TIP |
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21 |
Issue |
4 |
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2089-2098 |
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Impact Factor
This paper presents a simple distance estimation for implicit polynomial fitting. It is computed as the height of a simplex built between the point and the surface (i.e., a triangle in 2-D or a tetrahedron in 3-D), which is used as a coarse but reliable estimation of the orthogonal distance. The proposed distance can be described as a function of the coefficients of the implicit polynomial. Moreover, it is differentiable and has a smooth behavior . Hence, it can be used in any gradient-based optimization. In this paper, its use in a Levenberg-Marquardt framework is shown, which is particularly devoted for nonlinear least squares problems. The proposed estimation is a generalization of the gradient-based distance estimation, which is widely used in the literature. Experimental results, both in 2-D and 3-D data sets, are provided. Comparisons with state-of-the-art techniques are presented, showing the advantages of the proposed approach. |
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1057-7149 |
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Admin @ si @ RoS2012b; ADAS @ adas @ |
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1937 |
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Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
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Title |
Multiple target tracking for intelligent headlights control |
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Journal Article |
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Year |
2012 |
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IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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13 |
Issue |
2 |
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594-605 |
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Intelligent Headlights |
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Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
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1524-9050 |
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ADAS |
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Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g |
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1877 |
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Author |
David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Virtual and Real World Adaptation for Pedestrian Detection |
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Year |
2014 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
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4 |
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797-809 |
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Domain Adaptation; Pedestrian Detection |
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Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. |
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0162-8828 |
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ADAS; 600.057; 600.054; 600.076 |
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ADAS @ adas @ VML2014 |
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2275 |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg |
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Title |
Coloring Action Recognition in Still Images |
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Journal Article |
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Year |
2013 |
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International Journal of Computer Vision |
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IJCV |
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105 |
Issue |
3 |
Pages |
205-221 |
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In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. |
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Springer US |
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0920-5691 |
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CIC; ADAS; 600.057; 600.048 |
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Admin @ si @ KRW2013 |
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2285 |
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