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| Author | David Vazquez; David Geronimo; Antonio Lopez | ||||
| Title | The effect of the distance in pedestrian detection | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 149 | Issue | Pages | ||
| Keywords | Pedestrian Detection | ||||
| Abstract | Pedestrian accidents are one of the leading preventable causes of death. In order to reduce the number of accidents, in the last decade the pedestrian protection systems have been introduced, a special type of advanced driver assistance systems, in witch an on-board camera explores the road ahead for possible collisions with pedestrians in order to warn the driver or perform braking actions. As a result of the variability of the appearance, pose and size, pedestrian detection is a very challenging task. So many techniques, models and features have been proposed to solve the problem. As the appearance of pedestrians varies signi cantly as a function of distance, a system based on multiple classi ers specialized on diferent depths is likely to improve the overall performance with respect to a typical system based on a general detector. Accordingly, the main aim of this work is to explore the e ect of the distance in pedestrian detection. We have evaluated three pedestrian detectors (HOG, HAAR and EOH) in two di erent databases (INRIA and Daimler09) for two di erent sizes (small and big). By a extensive set of experiments we answer to questions like which datasets and evaluation methods are the most adequate, which is the best method for each size of the pedestrians and why or how do the method optimum parameters vary with respect to the distance | ||||
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Thesis | Master's thesis | |||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | M.Sc. | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ VGL2009 | Serial | 1669 | ||
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| Author | David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa | ||||
| Title | Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection | Type | Conference Article | ||
| Year | 2007 | Publication | Proceedings of the 5th International Conference on Computer Vision Systems | Abbreviated Journal | ICVS |
| Volume | Issue | Pages | |||
| Keywords | Pedestrian Detection | ||||
| Abstract | On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one. |
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| Address | Bielefeld (Germany) | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ gsl2007a | Serial | 786 | ||
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| Author | David Geronimo; Antonio Lopez; Angel Sappa | ||||
| Title | Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey | Type | Conference Article | ||
| Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 547–554 | |
| Keywords | Pedestrian detection | ||||
| Abstract | Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study. |
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| Address | Girona (Spain) | ||||
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Thesis | ||||
| Publisher | Place of Publication | Editor | J. Marti et al. | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GLS2007 | Serial | 804 | ||
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| Author | David Geronimo; Antonio Lopez; Daniel Ponsa; Angel Sappa | ||||
| Title | Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection | Type | Conference Article | ||
| Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 418–425 | |
| Keywords | Pedestrian detection | ||||
| Abstract | |||||
| Address | Girona (Spain) | ||||
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| Publisher | Place of Publication | Editor | J. Marti et al. | ||
| Language | Summary Language | Original Title | |||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GLP2007a | Serial | 805 | ||
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| Author | David Geronimo; Angel Sappa; Antonio Lopez | ||||
| Title | Stereo-based Candidate Generation for Pedestrian Protection Systems | Type | Book Chapter | ||
| Year | 2010 | Publication | Binocular Vision: Development, Depth Perception and Disorders | Abbreviated Journal | |
| Volume | Issue | 9 | Pages | 189–208 | |
| Keywords | Pedestrian Detection | ||||
| Abstract | This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided. | ||||
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| Publisher | NOVA Publishers | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSL2010 | Serial | 1301 | ||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez | ||||
| Title | Color Attributes for Object Detection | Type | Conference Article | ||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3306-3313 | ||
| Keywords | pedestrian detection | ||||
| Abstract | State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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| Address | Providence; Rhode Island; USA; | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS; CIC; | Approved | no | ||
| Call Number | Admin @ si @ KRW2012 | Serial | 1935 | ||
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| Author | Diego Cheda; Daniel Ponsa; Antonio Lopez | ||||
| Title | Pedestrian Candidates Generation using Monocular Cues | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
| Volume | Issue | Pages | 7-12 | ||
| Keywords | pedestrian detection | ||||
| Abstract | Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1931-0587 | ISBN | 978-1-4673-2119-8 | Medium | |
| Area | Expedition | Conference | IV | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d | Serial | 2013 | ||
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| Author | Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez | ||||
| Title | Moving object detection from mobile platforms using stereo data registration | Type | Book Chapter | ||
| Year | 2012 | Publication | Computational Intelligence paradigms in advanced pattern classification | Abbreviated Journal | |
| Volume | 386 | Issue | Pages | 25-37 | |
| Keywords | pedestrian detection | ||||
| Abstract | This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Marek R. Ogiela; Lakhmi C. Jain | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1860-949X | ISBN | 978-3-642-24048-5 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ SGD2012 | Serial | 2061 | ||
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| Author | Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez | ||||
| Title | Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE Intelligent Vehicles Symposium IV2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 356-361 | ||
| Keywords | Pedestrian Detection | ||||
| Abstract | Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. | ||||
| Address | Seoul; Corea; June 2015 | ||||
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| Area | ACDC | Expedition | Conference | IV | |
| Notes | ADAS; 600.076; 600.057; 600.054 | Approved | no | ||
| Call Number | ADAS @ adas @ GVX2015 | Serial | 2625 | ||
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| Author | Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez | ||||
| Title | 3d Pedestrian Detection via Random Forest | Type | Miscellaneous | ||
| Year | 2014 | Publication | European Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 231-238 | ||
| Keywords | Pedestrian Detection | ||||
| Abstract | Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications. |
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| Address | Zurich; suiza; September 2014 | ||||
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| Area | Expedition | Conference | ECCV-Demo | ||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ VRR2014 | Serial | 2570 | ||
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| Author | Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez | ||||
| Title | 3D-Guided Multiscale Sliding Window for Pedestrian Detection | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
| Volume | 9117 | Issue | Pages | 560-568 | |
| Keywords | Pedestrian Detection | ||||
| Abstract | The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy. | ||||
| Address | Santiago de Compostela; España; June 2015 | ||||
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| Area | ACDC | Expedition | Conference | IbPRIA | |
| Notes | ADAS; 600.076; 600.057; 600.054 | Approved | no | ||
| Call Number | ADAS @ adas @ GVR2015 | Serial | 2585 | ||
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