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David Geronimo, Angel Sappa, & Antonio Lopez. (2010). Stereo-based Candidate Generation for Pedestrian Protection Systems. In Binocular Vision: Development, Depth Perception and Disorders (189–208). NOVA Publishers.
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.
Keywords: Pedestrian Detection
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David Geronimo, Angel Sappa, Antonio Lopez, & Daniel Ponsa. (2007). Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection. In Proceedings of the 5th International Conference on Computer Vision Systems.
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.
Keywords: Pedestrian Detection
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David Geronimo, Angel Sappa, Antonio Lopez, & Daniel Ponsa. (2006). Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation.
Abstract: In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform
the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows.
With this approach, the system can work in urban roads, as will be illustrated by current results.
Keywords: ADAS, pedestrian detection, adaboost learning, pitch estimation, haar wavelets, edge orientation histograms.
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David Geronimo, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2010). 2D-3D based on-board pedestrian detection system. CVIU - Computer Vision and Image Understanding, 114(5), 583–595.
Abstract: During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system.
Keywords: Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms
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David Geronimo, & Antonio Lopez. (2014). Vision-based Pedestrian Protection Systems for Intelligent Vehicles. Springer Briefs in Computer Vision.
Abstract: Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented.
Keywords: Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users
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David Geronimo, & Antonio Lopez. (2010). Deteccion de Peatones para Sistemas Avanzados de Asistencia al Conductor.
Abstract: Los sistemas de asistencia al conductor, y particularmente los sistemas de protección de peatones, representan uno de los campos de investigación más activos dedicados a la mejora de la seguridad vial. El mayor desafío es el desarrollo de sistemas a bordo fiables de detección de peatones. En esta revisión del estado de la técnica de la detección de peatones, se divide el problema en diferentes etapas, cada una con responsabilidades propias dentro del sistema. Esta división facilita el posterior análisis y discusión de cada uno de los métodos en la literatura, favoreciendo la comparación entre ellos. Finalmente se discuten los temas más importantes de este campo poniendo especial énfasis en las necesidades actuales y los desafíos futuros.
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David Geronimo, & Antonio Lopez. (2010). Sistema de deteccion de peatones.
Abstract: Durante la próxima década, los sistemas de protección de peatones jugarán un papel fundamental en el reto de mejorar la seguridad viaria. El objetivo principal de estos sistemas, detectar peatones en entornos urbanos, implica procesar imágenes de escenas exteriores desde una plataforma móvil para buscar objetos de aspecto variable como son las personas. Dadas estas dificultades, estos sistemas hacen uso de las últimas técnicas de visión por computador. Esta propuesta consiste en un sistema de tres módulos basado tanto en información 2D como en 3D. El primer módulo utiliza información 3D para hacer una estimación de los parámetros de la carretera y seleccionar regiones de interés que serán analizadas después. El segundo módulo utiliza un clasificador de ventanas 2D para etiquetar las mencionadas regiones como peatón o no peatón. El módulo final vuelve a utilizar de nuevo la información 3D para verificar las regiones clasificadas y, con información 2D, refinar los resultados finales. Los resultados experimentales son positivos tanto en rendimiento como en tiempo de cómputo.
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David Geronimo, Antonio Lopez, & Angel Sappa. (2007). Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 (Vol. 1, 547–554).
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.
Keywords: Pedestrian detection
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David Geronimo, Antonio Lopez, Angel Sappa, & Thorsten Graf. (2010). Survey on Pedestrian Detection for Advanced Driver Assistance Systems. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 32(7), 1239–1258.
Abstract: Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.
Keywords: ADAS, pedestrian detection, on-board vision, survey
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David Geronimo, Antonio Lopez, Daniel Ponsa, & Angel Sappa. (2007). Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection. In J. Marti et al. (Ed.), 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 (Vol. 1, 418–425).
Keywords: Pedestrian detection
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David Geronimo, David Vazquez, & Arturo de la Escalera. (2017). Vision-Based Advanced Driver Assistance Systems. In Computer Vision in Vehicle Technology: Land, Sea, and Air.
Keywords: ADAS; Autonomous Driving
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David Geronimo, Frederic Lerasle, & Antonio Lopez. (2012). State-driven particle filter for multi-person tracking. In J. Blanc-Talon et al. (Ed.), 11th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 7517, pp. 467–478). Heidelberg: Springer.
Abstract: Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.
Keywords: human tracking
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David Geronimo, Joan Serrat, Antonio Lopez, & Ramon Baldrich. (2013). Traffic sign recognition for computer vision project-based learning. T-EDUC - IEEE Transactions on Education, 56(3), 364–371.
Abstract: This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback.
Keywords: traffic signs
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David Guillamet. (2004). Statistical Local Appearance Models for Object Recognition (Jordi Vitria, Ed.). Ph.D. thesis, , .
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David Guillamet. (2001). Color histogram classification using NMF.
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