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Author |
Angel Sappa; George A. Triantafyllid |
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Title |
Computer Graphics and Imaging |
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2012 |
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Computer Graphics and Imaging |
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Crete, Greece |
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978-0-88986-921-9 |
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ADAS |
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no |
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Admin @ si @ Sap2012 |
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2067 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez |
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Title |
Moving object detection from mobile platforms using stereo data registration |
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Year |
2012 |
Publication |
Computational Intelligence paradigms in advanced pattern classification |
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Volume |
386 |
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25-37 |
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Keywords |
pedestrian detection |
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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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Springer Berlin Heidelberg |
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Marek R. Ogiela; Lakhmi C. Jain |
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1860-949X |
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978-3-642-24048-5 |
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no |
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Admin @ si @ SGD2012 |
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2061 |
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Author |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
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Title |
Pedestrian Detection: Exploring Virtual Worlds |
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Book Chapter |
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Year |
2012 |
Publication |
Handbook of Pattern Recognition: Methods and Application |
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5 |
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145-162 |
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Keywords |
Virtual worlds; Pedestrian Detection; Domain Adaptation |
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Abstract |
Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
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iConcept Press |
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English |
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978-1-477554-82-1 |
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no |
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Call Number |
ADAS @ adas @ MGV2012 |
Serial |
1979 |
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Author |
Ferran Diego |
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Title |
Probabilistic Alignment of Video Sequences Recorded by Moving Cameras |
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Book Whole |
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Year |
2011 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Video alignment consists of integrating multiple video sequences recorded independently into a single video sequence. This means to register both in time (synchronize
frames) and space (image registration) so that the two videos sequences can be fused
or compared pixel–wise. In spite of being relatively unknown, many applications today may benefit from the availability of robust and efficient video alignment methods.
For instance, video surveillance requires to integrate video sequences that are recorded
of the same scene at different times in order to detect changes. The problem of aligning videos has been addressed before, but in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, most works rely
on restrictive assumptions which reduce its difficulty such as linear time correspondence or the knowledge of the complete trajectories of corresponding scene points on the images; to some extent, these assumptions limit the practical applicability of the solutions developed until now. In this thesis, we focus on the challenging problem of aligning sequences recorded at different times from independent moving cameras following similar but not coincident trajectories. More precisely, this thesis covers four studies that advance the state-of-the-art in video alignment. First, we focus on analyzing and developing a probabilistic framework for video alignment, that is, a principled way to integrate multiple observations and prior information. In this way, two different approaches are presented to exploit the combination of several purely visual features (image–intensities, visual words and dense motion field descriptor), and
global positioning system (GPS) information. Second, we focus on reformulating the
problem into a single alignment framework since previous works on video alignment
adopt a divide–and–conquer strategy, i.e., first solve the synchronization, and then
register corresponding frames. This also generalizes the ’classic’ case of fixed geometric transform and linear time mapping. Third, we focus on exploiting directly the
time domain of the video sequences in order to avoid exhaustive cross–frame search.
This provides relevant information used for learning the temporal mapping between
pairs of video sequences. Finally, we focus on adapting these methods to the on–line
setting for road detection and vehicle geolocation. The qualitative and quantitative
results presented in this thesis on a variety of real–world pairs of video sequences show that the proposed method is: robust to varying imaging conditions, different image
content (e.g., incoming and outgoing vehicles), variations on camera velocity, and
different scenarios (indoor and outdoor) going beyond the state–of–the–art. Moreover, the on–line video alignment has been successfully applied for road detection and
vehicle geolocation achieving promising results. |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Joan Serrat |
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ADAS |
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no |
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Call Number |
Admin @ si @ Die2011 |
Serial |
1787 |
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Author |
Aura Hernandez-Sabate; Debora Gil |
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Title |
The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries |
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Book Chapter |
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Year |
2012 |
Publication |
Intravascular Ultrasound |
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185-206 |
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Intech |
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Editor |
Yasuhiro Honda |
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English |
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english |
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978-953-307-900-4 |
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IAM; ADAS |
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no |
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Call Number |
IAM @ iam @ HeG2012 |
Serial |
1684 |
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Author |
Angel Sappa (ed) |
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Title |
Computer Graphics and Imaging |
Type |
Book Whole |
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Year |
2010 |
Publication |
Computer Graphics and Imaging |
Abbreviated Journal |
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Editor |
Angel Sappa |
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978–0–88986–836–6 |
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CGIM |
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ADAS |
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no |
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ADAS @ adas @ Sap2010 |
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1468 |
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Author |
Jose Manuel Alvarez |
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Title |
Combining Context and Appearance for Road Detection |
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Book Whole |
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2010 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the
development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit
color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene.
In this way, we present two different approaches to infer the geometry of the road ahead
the moving vehicle. Finally, we focus on combining these context and appearance (color)
approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Antonio Lopez;Theo Gevers |
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978-84-937261-8-8 |
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ADAS |
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no |
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Call Number |
Admin @ si @ Alv2010 |
Serial |
1454 |
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Author |
David Geronimo; Angel Sappa; Antonio Lopez |
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Title |
Stereo-based Candidate Generation for Pedestrian Protection Systems |
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2010 |
Publication |
Binocular Vision: Development, Depth Perception and Disorders |
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9 |
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189–208 |
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Pedestrian Detection |
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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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NOVA Publishers |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ GSL2010 |
Serial |
1301 |
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Author |
David Geronimo |
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Title |
A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems |
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Book Whole |
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Year |
2010 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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At the beginning of the 21th century, traffic accidents have become a major problem not only for developed countries but also for emerging ones. As in other scientific areas in which Artificial Intelligence is becoming a key actor, advanced driver assistance systems, and concretely pedestrian protection systems based on Computer Vision, are becoming a strong topic of research aimed at improving the safety of pedestrians. However, the challenge is of considerable complexity due to the varying appearance of humans (e.g., clothes, size, aspect ratio, shape, etc.), the dynamic nature of on-board systems and the unstructured moving environments that urban scenarios represent. In addition, the required performance is demanding both in terms of computational time and detection rates. In this thesis, instead of focusing on improving specific tasks as it is frequent in the literature, we present a global approach to the problem. Such a global overview starts by the proposal of a generic architecture to be used as a framework both to review the literature and to organize the studied techniques along the thesis. We then focus the research on tasks such as foreground segmentation, object classification and refinement following a general viewpoint and exploring aspects that are not usually analyzed. In order to perform the experiments, we also present a novel pedestrian dataset that consists of three subsets, each one addressed to the evaluation of a different specific task in the system. The results presented in this thesis not only end with a proposal of a pedestrian detection system but also go one step beyond by pointing out new insights, formalizing existing and proposed algorithms, introducing new techniques and evaluating their performance, which we hope will provide new foundations for future research in the area. |
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Address |
Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras |
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978-84-936529-5-1 |
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ADAS |
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ADAS @ adas @ Ger2010 |
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1279 |
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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Title |
Prior Knowledge Based Motion Model Representation |
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Book Chapter |
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2009 |
Publication |
Progress in Computer Vision and Image Analysis |
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16 |
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Horst Bunke; JuanJose Villanueva; Gemma Sanchez |
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ADAS |
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Call Number |
ADAS @ adas @ SAM2009 |
Serial |
1235 |
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