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Author (down) Zhijie Fang; David Vazquez; Antonio Lopez edit  doi
openurl 
  Title On-Board Detection of Pedestrian Intentions Type Journal Article
  Year 2017 Publication Sensors Abbreviated Journal SENS  
  Volume 17 Issue 10 Pages 2193  
  Keywords pedestrian intention; ADAS; self-driving  
  Abstract Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information.
 
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  Notes ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 Approved no  
  Call Number Admin @ si @ FVL2017 Serial 2983  
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Author (down) Zhijie Fang; Antonio Lopez edit  url
doi  openurl
  Title Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation Type Journal Article
  Year 2019 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume Issue Pages  
  Keywords  
  Abstract Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.  
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  Notes ADAS; no proj Approved no  
  Call Number Admin @ si @ FaL2019 Serial 3305  
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Author (down) Yu Jie; Jaume Amores; N. Sebe; Petia Radeva; Tian Qi edit  openurl
  Title Distance Learning for Similarity Estimation Type Journal
  Year 2008 Publication IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462 Abbreviated Journal  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ JAS2008 Serial 961  
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Author (down) Xavier Soria; Angel Sappa; Riad I. Hammoud edit  url
doi  openurl
  Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Type Journal Article
  Year 2018 Publication Sensors Abbreviated Journal SENS  
  Volume 18 Issue 7 Pages 2059  
  Keywords RGB-NIR sensor; multispectral imaging; deep learning; CNNs  
  Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
 
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  Notes ADAS: MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SSH2018 Serial 3145  
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Author (down) Xavier Boix; Josep M. Gonfaus; Joost van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
  Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 96 Issue 1 Pages 83-102  
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  Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
 
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  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes CAT;ISE;CIC;ADAS Approved no  
  Call Number Admin @ si @ BGW2012 Serial 1718  
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Author (down) Victor Santos; Angel Sappa; Miguel Oliveira edit  openurl
  Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
  Year 2017 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 91 Issue Pages 208-209  
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  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ SSO2017 Serial 2915  
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Author (down) T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo edit  doi
openurl 
  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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  Series Volume Series Issue Edition  
  ISSN 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 (down) Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester edit   pdf
url  openurl
  Title Multilocal Creaseness Measure Type Journal
  Year 2012 Publication The Insight Journal Abbreviated Journal IJ  
  Volume Issue Pages  
  Keywords Ridges, Valley, Creaseness, Structure Tensor, Skeleton,  
  Abstract This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.  
  Address  
  Corporate Author Alma IT Systems Thesis  
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  Language english Summary Language english Original Title  
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  Notes IAM;ADAS; Approved no  
  Call Number IAM @ iam @ VGL2012 Serial 1840  
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Author (down) P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris Vintimilla; Angel Sappa edit  doi
openurl 
  Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication Sensors Abbreviated Journal SENS  
  Volume 14 Issue 2 Pages 3690-3701  
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  Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.  
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  Notes ADAS;600.055; 600.076 Approved no  
  Call Number Admin @ si @ RCA2014a Serial 2474  
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Author (down) Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras edit  url
openurl 
  Title Segmentation of aerial images for plausible detail synthesis Type Journal Article
  Year 2018 Publication Computers & Graphics Abbreviated Journal CG  
  Volume 71 Issue Pages 23-34  
  Keywords Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation  
  Abstract The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.  
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  Series Volume Series Issue Edition  
  ISSN 0097-8493 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ ACC2018 Serial 3147  
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