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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa
Title Multiple target tracking for intelligent headlights control Type Journal Article
Year 2012 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 13 Issue 2 Pages 594-605
Keywords Intelligent Headlights
Abstract 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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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g Serial 1877
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Author Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca
Title Toward Real-Time Pedestrian Detection Based on a Deformable Template Model Type Journal Article
Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 15 Issue 1 Pages 355-364
Keywords
Abstract Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1524-9050 ISBN Medium
Area Expedition Conference
Notes ISE; 601.213; 600.078 Approved no
Call Number PGH2014 Serial 2350
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Author Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez
Title Road Geometry Classification by Adaptative Shape Models Type Journal Article
Year 2013 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 14 Issue 1 Pages 459-468
Keywords road detection
Abstract Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS;ISE Approved no
Call Number Admin @ si @ AGD2013;; ADAS @ adas @ Serial 2269
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Author Naveen Onkarappa; Angel Sappa
Title Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios Type Journal Article
Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 15 Issue 1 Pages 136-147
Keywords
Abstract IF: 3.064
Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ OnS2014a Serial 2386
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Author Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras
Title Combining Priors, Appearance and Context for Road Detection Type Journal Article
Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 15 Issue 3 Pages 1168-1178
Keywords Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout
Abstract 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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Publisher Place of Publication Editor IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.076;ISE Approved no
Call Number Admin @ si @ ALG2014 Serial 2501
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Author T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo
Title Multi-Spectral Stereo Odometry Type Journal Article
Year 2015 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 16 Issue 3 Pages 1210-1224
Keywords Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery
Abstract In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based
on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating
Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number Admin @ si @ MAS2015a Serial 2533
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Author S. Chanda; Umapada Pal; Oriol Ramos Terrades
Title Word-Wise Thai and Roman Script Identification Type Journal
Year 2009 Publication ACM Transactions on Asian Language Information Processing Abbreviated Journal TALIP
Volume 8 Issue 3 Pages 1-21
Keywords
Abstract In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1530-0226 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ CPR2009f Serial 1869
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Error-Correcting Output Codes Library Type Journal Article
Year 2010 Publication Journal of Machine Learning Research Abbreviated Journal JMLR
Volume 11 Issue Pages 661-664
Keywords
Abstract (Feb):661&#8722;664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN (up) 1532-4435 ISBN Medium
Area Expedition Conference
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2010c Serial 1286
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Author Jordi Roca; A.Owen; G.Jordan; Y.Ling; C. Alejandro Parraga; A.Hurlbert
Title Inter-individual Variations in Color Naming and the Structure of 3D Color Space Type Abstract
Year 2011 Publication Journal of Vision Abbreviated Journal VSS
Volume 12 Issue 2 Pages 166
Keywords
Abstract 36.307
Many everyday behavioural uses of color vision depend on color naming ability, which is neither measured nor predicted by most standardized tests of color vision, for either normal or anomalous color vision. Here we demonstrate a new method to quantify color naming ability by deriving a compact computational description of individual 3D color spaces. Methods: Individual observers underwent standardized color vision diagnostic tests (including anomaloscope testing) and a series of custom-made color naming tasks using 500 distinct color samples, either CRT stimuli (“light”-based) or Munsell chips (“surface”-based), with both forced- and free-choice color naming paradigms. For each subject, we defined his/her color solid as the set of 3D convex hulls computed for each basic color category from the relevant collection of categorised points in perceptually uniform CIELAB space. From the parameters of the convex hulls, we derived several indices to characterise the 3D structure of the color solid and its inter-individual variations. Using a reference group of 25 normal trichromats (NT), we defined the degree of normality for the shape, location and overlap of each color region, and the extent of “light”-“surface” agreement. Results: Certain features of color perception emerge from analysis of the average NT color solid, e.g.: (1) the white category is slightly shifted towards blue; and (2) the variability in category border location across NT subjects is asymmetric across color space, with least variability in the blue/green region. Comparisons between individual and average NT indices reveal specific naming “deficits”, e.g.: (1) Category volumes for white, green, brown and grey are expanded for anomalous trichromats and dichromats; and (2) the focal structure of color space is disrupted more in protanopia than other forms of anomalous color vision. The indices both capture the structure of subjective color spaces and allow us to quantify inter-individual differences in color naming ability.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1534-7362 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ ROJ2011 Serial 1758
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Author C. Alejandro Parraga; Jordi Roca; Maria Vanrell
Title Do Basic Colors Influence Chromatic Adaptation? Type Journal Article
Year 2011 Publication Journal of Vision Abbreviated Journal VSS
Volume 11 Issue 11 Pages 85
Keywords
Abstract Color constancy (the ability to perceive colors relatively stable under different illuminants) is the result of several mechanisms spread across different neural levels and responding to several visual scene cues. It is usually measured by estimating the perceived color of a grey patch under an illuminant change. In this work, we hypothesize whether chromatic adaptation (without a reference white or grey) could be driven by certain colors, specifically those corresponding to the universal color terms proposed by Berlin and Kay (1969). To this end we have developed a new psychophysical paradigm in which subjects adjust the color of a test patch (in CIELab space) to match their memory of the best example of a given color chosen from the universal terms list (grey, red, green, blue, yellow, purple, pink, orange and brown). The test patch is embedded inside a Mondrian image and presented on a calibrated CRT screen inside a dark cabin. All subjects were trained to “recall” their most exemplary colors reliably from memory and asked to always produce the same basic colors when required under several adaptation conditions. These include achromatic and colored Mondrian backgrounds, under a simulated D65 illuminant and several colored illuminants. A set of basic colors were measured for each subject under neutral conditions (achromatic background and D65 illuminant) and used as “reference” for the rest of the experiment. The colors adjusted by the subjects in each adaptation condition were compared to the reference colors under the corresponding illuminant and a “constancy index” was obtained for each of them. Our results show that for some colors the constancy index was better than for grey. The set of best adapted colors in each condition were common to a majority of subjects and were dependent on the chromaticity of the illuminant and the chromatic background considered.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1534-7362 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ PRV2011 Serial 1759
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title Top-Down Color Attention for Object Recognition Type Conference Article
Year 2009 Publication 12th International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 979 - 986
Keywords
Abstract Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
Address Kyoto, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1550-5499 ISBN 978-1-4244-4420-5 Medium
Area Expedition Conference ICCV
Notes CIC Approved no
Call Number CAT @ cat @ SWV2009 Serial 1196
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Author Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez
Title Detection and Removal of Chromatic Moving Shadows in Surveillance Scenarios Type Conference Article
Year 2009 Publication 12th International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 1499 - 1506
Keywords
Abstract Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.
Address Kyoto, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1550-5499 ISBN 978-1-4244-4420-5 Medium
Area Expedition Conference ICCV
Notes Approved no
Call Number ISE @ ise @ HHM2009 Serial 1213
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Author Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders
Title Segmentation as Selective Search for Object Recognition Type Conference Article
Year 2011 Publication 13th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 1879-1886
Keywords
Abstract For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge.
Address Barcelona
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1550-5499 ISBN 978-1-4577-1101-5 Medium
Area Expedition Conference ICCV
Notes ISE Approved no
Call Number Admin @ si @ SUG2011 Serial 1780
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Author Shida Beigpour; Joost Van de Weijer
Title Object Recoloring Based on Intrinsic Image Estimation Type Conference Article
Year 2011 Publication 13th IEEE International Conference in Computer Vision Abbreviated Journal
Volume Issue Pages 327 - 334
Keywords
Abstract Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods.
Address Barcelona
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1550-5499 ISBN 978-1-4577-1101-5 Medium
Area Expedition Conference ICCV
Notes CIC Approved no
Call Number Admin @ si @ BeW2011 Serial 1781
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Author Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca
Title A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes Type Conference Article
Year 2011 Publication 13th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 1776-1783
Keywords
Abstract Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance.
Address Barcelona
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) 1550-5499 ISBN 978-1-4577-1101-5 Medium
Area Expedition Conference ICCV
Notes ISE Approved no
Call Number Admin @ si @ CHM2011 Serial 1811
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