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Author 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 (down) 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 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 (down) 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 Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
doi  openurl
  Title Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal (down) RAS  
  Volume 83 Issue Pages 312-325  
  Keywords Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives  
  Abstract When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. 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. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.  
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  Publisher Elsevier B.V. Place of Publication Editor  
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  Notes ADAS; 600.086, 600.076 Approved no  
  Call Number Admin @ si @OSS2016a Serial 2806  
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Author Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris Vintimilla; Ricardo Toledo edit   pdf
doi  openurl
  Title Monocular visual odometry: A cross-spectral image fusion based approach Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal (down) RAS  
  Volume 85 Issue Pages 26-36  
  Keywords Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion  
  Abstract This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.  
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  Notes ADAS;600.086; 600.076 Approved no  
  Call Number Admin @ si @SAC2016 Serial 2811  
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
url  openurl
  Title Incremental texture mapping for autonomous driving Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal (down) RAS  
  Volume 84 Issue Pages 113-128  
  Keywords Scene reconstruction; Autonomous driving; Texture mapping  
  Abstract Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.  
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  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ OSS2016b Serial 2912  
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Author 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 (down) 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 A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva edit  doi
openurl 
  Title Topological principal component analysis for face encoding and recognition Type Journal Article
  Year 2001 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 22 Issue 6-7 Pages 769–776  
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  Abstract IF: 0.552  
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  Notes ADAS;OR;MV Approved no  
  Call Number ADAS @ adas @ PVL2001 Serial 155  
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Author Jaume Amores; Petia Radeva edit  url
doi  openurl
  Title Registration and Retrieval of Highly Elastic Bodies using Contextual Information Type Journal Article
  Year 2005 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 26 Issue 11 Pages 1720–1731  
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  Abstract IF: 1.138  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ AmR2005b Serial 592  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  doi
openurl 
  Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 27 Issue 3 Pages 201–209  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2006 Serial 643  
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Author Fadi Dornaika; Angel Sappa edit  doi
openurl 
  Title Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models Type Journal Article
  Year 2007 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 28 Issue 15 Pages 2116-2126  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DoS2007c Serial 877  
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