Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

Abstract: 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