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Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nikolay; B. Ter Haar Romeny; Juan J. Villanueva; M. Viergerver |
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New improvements in the multiscale analysis of trabecular bone patterns |
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2000 |
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Pattern Recognition and Applications |
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251-260 |
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IOS Press |
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ADAS |
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no |
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Admin @ si @ |
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3418 |
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Author |
Felipe Codevilla |
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Title |
On Building End-to-End Driving Models Through Imitation Learning |
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Year |
2019 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Autonomous vehicles are now considered as an assured asset in the future. Literally, all the relevant car-markers are now in a race to produce fully autonomous vehicles. These car-makers usually make use of modular pipelines for designing autonomous vehicles. This strategy decomposes the problem in a variety of tasks such as object detection and recognition, semantic and instance segmentation, depth estimation, SLAM and place recognition, as well as planning and control. Each module requires a separate set of expert algorithms, which are costly specially in the amount of human labor and necessity of data labelling. An alternative, that recently has driven considerable interest, is the end-to-end driving. In the end-to-end driving paradigm, perception and control are learned simultaneously using a deep network. These sensorimotor models are typically obtained by imitation learning fromhuman demonstrations. The main advantage is that this approach can directly learn from large fleets of human-driven vehicles without requiring a fixed ontology and extensive amounts of labeling. However, scaling end-to-end driving methods to behaviors more complex than simple lane keeping or lead vehicle following remains an open problem. On this thesis, in order to achieve more complex behaviours, we
address some issues when creating end-to-end driving system through imitation
learning. The first of themis a necessity of an environment for algorithm evaluation and collection of driving demonstrations. On this matter, we participated on the creation of the CARLA simulator, an open source platformbuilt from ground up for autonomous driving validation and prototyping. Since the end-to-end approach is purely reactive, there is also the necessity to provide an interface with a global planning system. With this, we propose the conditional imitation learning that conditions the actions produced into some high level command. Evaluation is also a concern and is commonly performed by comparing the end-to-end network output to some pre-collected driving dataset. We show that this is surprisingly weakly correlated to the actual driving and propose strategies on how to better acquire data and a better comparison strategy. Finally, we confirmwell-known generalization issues
(due to dataset bias and overfitting), new ones (due to dynamic objects and the
lack of a causal model), and training instability; problems requiring further research before end-to-end driving through imitation can scale to real-world driving. |
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May 2019 |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez |
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ADAS; 600.118 |
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no |
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Call Number |
Admin @ si @ Cod2019 |
Serial |
3387 |
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Author |
Naveen Onkarappa |
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Title |
Optical Flow in Driver Assistance Systems |
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Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Motion perception is one of the most important attributes of the human brain. Visual motion perception consists in inferring speed and direction of elements in a scene based on visual inputs. Analogously, computer vision is assisted by motion cues in the scene. Motion detection in computer vision is useful in solving problems such as segmentation, depth from motion, structure from motion, compression, navigation and many others. These problems are common in several applications, for instance, video surveillance, robot navigation and advanced driver assistance systems (ADAS). One of the most widely used techniques for motion detection is the optical flow estimation. The work in this thesis attempts to make optical flow suitable for the requirements and conditions of driving scenarios. In this context, a novel space-variant representation called reverse log-polar representation is proposed that is shown to be better than the traditional log-polar space-variant representation for ADAS. The space-variant representations reduce the amount of data to be processed. Another major contribution in this research is related to the analysis of the influence of specific characteristics from driving scenarios on the optical flow accuracy. Characteristics such as vehicle speed and
road texture are considered in the aforementioned analysis. From this study, it is inferred that the regularization weight has to be adapted according to the required error measure and for different speeds and road textures. It is also shown that polar represented optical flow suits driving scenarios where predominant motion is translation. Due to the requirements of such a study and by the lack of needed datasets a new synthetic dataset is presented; it contains: i) sequences of different speeds and road textures in an urban scenario; ii) sequences with complex motion of an on-board camera; and iii) sequences with additional moving vehicles in the scene. The ground-truth optical flow is generated by the ray-tracing technique. Further, few applications of optical flow in ADAS are shown. Firstly, a robust RANSAC based technique to estimate horizon line is proposed. Then, an egomotion estimation is presented to compare the proposed space-variant representation with the classical one. As a final contribution, a modification in the regularization term is proposed that notably improves the results
in the ADAS applications. This adaptation is evaluated using a state of the art optical flow technique. The experiments on a public dataset (KITTI) validate the advantages of using the proposed modification. |
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Address |
Bellaterra |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Angel Sappa |
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978-84-940902-1-9 |
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ADAS |
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no |
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Call Number |
Admin @ si @ Nav2013 |
Serial |
2447 |
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Author |
Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat |
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Title |
Pattern Recognition and Image Analysis |
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Year |
2007 |
Publication |
3rd Iberian Conference |
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Volume |
6669 |
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4477-4478 |
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Girona (Spain) |
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IbPRIA |
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ADAS |
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Call Number |
ADAS @ adas @ MBM2007 |
Serial |
994 |
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Author |
Javier Marin |
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Title |
Pedestrian Detection Based on Local Experts |
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Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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During the last decade vision-based human detection systems have started to play a key rolein multiple applications linked to driver assistance, surveillance, robot sensing and home automation.
Detecting humans is by far one of the most challenging tasks in Computer Vision.
This is mainly due to the high degree of variability in the human appearanceassociated to
the clothing, pose, shape and size. Besides, other factors such as cluttered scenarios, partial occlusions, or environmental conditions can make the detection task even harder.
Most promising methods of the state-of-the-art rely on discriminative learning paradigms which are fed with positive and negative examples. The training data is one of the most
relevant elements in order to build a robust detector as it has to cope the large variability of the target. In order to create this dataset human supervision is required. The drawback at this point is the arduous effort of annotating as well as looking for such claimed variability.
In this PhD thesis we address two recurrent problems in the literature. In the first stage,we aim to reduce the consuming task of annotating, namely, by using computer graphics.
More concretely, we develop a virtual urban scenario for later generating a pedestrian dataset.
Then, we train a detector using this dataset, and finally we assess if this detector can be successfully applied in a real scenario.
In the second stage, we focus on increasing the robustness of our pedestrian detectors
under partial occlusions. In particular, we present a novel occlusion handling approach to increase the performance of block-based holistic methods under partial occlusions. For this purpose, we make use of local experts via a RandomSubspaceMethod (RSM) to handle these cases. If the method infers a possible partial occlusion, then the RSM, based on performance statistics obtained from partially occluded data, is applied. The last objective of this thesis
is to propose a robust pedestrian detector based on an ensemble of local experts. To achieve this goal, we use the random forest paradigm, where the trees act as ensembles an their nodesare the local experts. In particular, each expert focus on performing a robust classification ofa pedestrian body patch. This approach offers computational efficiency and far less design complexity when compared to other state-of-the-artmethods, while reaching better accuracy |
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Address |
Barcelona |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez;Jaume Amores |
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ADAS |
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Call Number |
Admin @ si @ Mar2013 |
Serial |
2280 |
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Author |
Antonio Lopez |
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Title |
Pedestrian Detection Systems |
Type |
Book Chapter |
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Year |
2018 |
Publication |
Wiley Encyclopedia of Electrical and Electronics Engineering |
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Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. |
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ADAS; 600.118 |
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no |
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Call Number |
Admin @ si @ Lop2018 |
Serial |
3230 |
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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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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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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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ADAS |
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Call Number |
ADAS @ adas @ MGV2012 |
Serial |
1979 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Photometric Invariance by Machine Learning |
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2012 |
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Color in Computer Vision: Fundamentals and Applications |
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7 |
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113-134 |
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road detection |
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iConcept Press Ltd |
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Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek |
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978-0-470-89084-4 |
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ADAS |
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no |
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Call Number |
Admin @ si @ AlL2012 |
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2186 |
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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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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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ADAS @ adas @ SAM2009 |
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1235 |
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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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2011 |
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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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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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