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A novel approach for vehicle and pedestrian detection based on data fusion techniques is presented. The work fuses information from a 2D laser scanner and a computer camera, to provide detection and classification of vehicles and pedestrians in road environments. Thanks to the data fusion approach, the limitations of each sensor are overcome. Thus reliable system is provided, fulfilling the demands of road safety applications. Classification is performed using each sensor independently. Laser scanner approach is based in pattern matching and vision approach is based in the classical Histogram of Oriented Gradients features approach. A higher stage performs data fusion using Kalman Filter and Global Nearest Neighbors. ; This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGAUTO-II (S2009/DPI-1509).

Abstract: The proposed work, depicts a novel algorithm able to provide multiple pedestrian detection, based on the use of classical sensors in modern automotive application and context information. The work takes advantage of the use of Joint Probabilities Data Association (JPDA) and context information to enhance the classic performance of the pedestrian detection algorithms. The combination of the different information sources with powerful tracking algorithms helps to overcome the difficulties of this processes, providing a trustable tool that improves performance of the single sensor detection algorithms. Context in a rich information source, able to improve the fusion process in all levels by the use of a priori knowledge of the application. In the present work multilevel fusion solution is provided for road safety application. Context is used in all the fusion levels, helping to improve the perception of the road environment and the relations among detections. By the fusion of all information sources, accurate and trustable detection is provided and complete situation assessment obtained, with estimation of the danger that involves any detection. ; This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGVAUTO-II ( S2009/DPI-1509).

Abstract: Fusion is becoming a classic topic in Intelligent Transport System (ITS) society. The lack of trustworthy sensors requires the combination of several devices to provide reliable detections. In this paper a novel approach, that takes advantage of the Joint Probabilistic Data Association technique (JPDA) for data association, is presented. The approach uses one of the most powerful techniques of Multiple Target Tracking theory and adapts it to fulfill the strong requirements of road safety applications. The different test performed proved that a powerful association technique can enhance the capacity of Advance Driver Assistance Systems. Two main sensors are used for pedestrian detection: laser scanner and computer vision. Furthermore, the approach takes advantage of the availability of other information sources i.e. context information and online information (GPS). The detections are fused using JPDA, enhancing the capacities of classical pedestrian detection systems, mainly based in visual information. The test performed also showed that JPDA improved the results offered by other data association techniques, e.g. Global Nearest Neighbors. ; This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03- 01) and (GRANT TRA2011-29454-C03-02). CAM through SEGAUTO-II ( S2009/DPI-1509) .

Abstract: Fusion is a common topic nowadays in Advanced Driver Assistance Systems (ADAS). The demanding requirements of safety applications require trustable sensing technologies. Fusion allows to provide trustable detections by combining different sensor devices, fulfilling the requirements of safety applications. High level fusion scheme is presented; able to improve classic ADAS systems by combining different sensing technologies i.e. laser scanner and computer vision. By means of powerful Data Fusion (DF) algorithms, the performance of classic ADAS detection systems is enhanced. Fusion is performed in a decentralized scheme (high level), allowing scalability; hence new sensing technologies can easily be added to increase the trustability and the accuracy of the overall system. Present work focus in the Data Fusion scheme used to combine the information of the sensors at high level. Although for completeness some details of the different detection algorithms (low level) of the different sensors is provided. The proposed work adapts a powerful Data Association technique for Multiple Targets Tracking (MTT): Joint Probabilistic Data Association (JPDA) to improve the trustability of the ADAS detection systems. The final application provides real time detection of road users (pedestrians and vehicles) in real road situations. The tests performed proved the improvement of the use of Data Fusion algorithms. Furthermore, comparison with other classic algorithms such as Global Nearest Neighbors (GNN) proved the performance of the overall architecture. ; This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGAUTO-II (S2009/DPI-1509) .

This paper presents an approach on detection of largely occluded pedestrians. From a pair of synchronized cameras in the Visible Light (VL) and Far Infrared (FIR) spectrum individual detections are combined and final confidence is inferred using a small set of logic rules via a Markov Logic Network. Pedestrians not entirely contained in the image or occluded are detected based on the binary classification on subparts of the detection window. The presented method is applied to a pedestrian classification problem in urban environments. The classifier has been tested in an Intelligent Transportation System (ITS) platform as part of an Advanced Driver Assistance Systems (ADAS). ; This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03- 01) and Driver Distraction Detector System (GRANT TRA2011-29454-C03-02), and by the Comunidad de Madrid through the project SEGVAUTO (S2009/DPI- 1509).

Proceeding of: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems October 7-12, 2012. Vilamoura, Algarve, Portugal. ; In this paper the authors propose a pedestrian detection system based on discrete features in infrared images. Unique keypoints are searched for in the images around which a descriptor, based on the histogram of the phase congruency orientation, is extracted. These descriptors are matched with defined regions of the body of a pedestrian. In case of a match, it creates a region of interest in the image, which is classified as a pedestrian / non-pedestrian by an SVM classifier. The pedestrian detection system has been tested in an advanced driver assistance system for urban driving. ; This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03- 01) and Driver Distraction Detector System (GRANT TRA2011-29454-C03-02), and by the Comunidad de Madrid through the project SEGVAUTO (S2009/DPI- 1509). ; Publicado

Proceeding of: 2012 IEEE Intelligent Vehicles Symposium (IV), Alcalá de Henares, Spain, June 3-7, 2012 ; In this paper a new contrast invariant descriptor for human detection in long-wave infrared images is proposed. It exploits local information histogram of orientations of phase coherence. Contrast in infrared images depends on the temperature of the object and the background, which makes gradient based descriptors less robust, especially in daylight conditions. The objective is to obtain a scale, brightness and contrast invariant descriptor that can successfully detect pedestrians in images taken with a cheap, temperature-sensitive, uncooled microbolometer. The descriptor, packed into grids is feed to a Support Vector Machine classifier. The algorithm has been tested in night and day sequences and its performance is compared with a day only descriptor: the histogram of oriented features (HOG). ; This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010- 20225-C03-01) and VIDAS-Driver (GRANT TRA2010- 21371-C03-02), and the Comunidad de Madrid through the project SEGVAUTO (S2009/DPI-1509). ; Publicado

Proceeding of: EUROCAST 2011: 13th International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 6-11, 2011 ; Traditionally obstacles detection is a great topic in computer vision applied to robotics navigation or advance driver assistance system (ADAS). Although other technologies, such as laser, obtain good results to detect obstacles in different environments, stereo vision has the advantage of providing 3D information, improving the knowledge of the environment. A study of the implementation of the u-v disparity in urban environments is presented in this paper, where several tests have been done in different situations which may be difficult to interpret by using a straightforward analysis of the u-v disparity in order to model the environment. ; This work also was supported by Spanish Government through the CICYT projects FEDORA (Grant TRA2010-20255-C03-01) and VIDASDRIVER (Grant TRA2010-21371-C03-02). ; Publicado

Recognizing the identity of a query individual in a surveillance sequence is the core of Multi-Object Tracking (MOT) and Re-Identification (Re-Id) algorithms. Both tasks can be addressed by measuring the appearance affinity between people observations with a deep neural model. Nevertheless, the differences in their specifications and, consequently, in the characteristics and constraints of the available training data for each one of these tasks, arise from the necessity of employing different learning approaches to attain each one of them. This article offers a comparative view of the Double-Margin-Contrastive and the Triplet loss function, and analyzes the benefits and drawbacks of applying each one of them to learn an Appearance Affinity model for Tracking and Re-Identification. A batch of experiments have been conducted, and their results support the hypothesis concluded from the presented study: Triplet loss function is more effective than the Contrastive one when an Re-Id model is learnt, and, conversely, in the MOT domain, the Contrastive loss can better discriminate between pairs of images rendering the same person or not. ; This research was funded by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21), Universidad Carlos III of Madrid through (PEAVAUTO-CM-UC3M), the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362), and the Ministerio de Educación, Cultura y Deporte para la Formación de Profesorado Universitario (FPU14/02143).

The advances in microelectronics foster the Unmanned Aerial Vehicles (UAVs) to be used in many civil and academic applications that require higher levels of autonomy and flight stabilization. The main objective in this paper is to provide UAVs with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor and indoor environments, mainly based on visual information that is captured by monocular cameras. This paper covers the topics of Pose Estimation, Navigation Guidance, and Visual Servoing. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and the results prove the improvement in the accuracy and the robustness of the proposed algorithms. ; Research supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R), and the Comunidad de Madrid through SEGVAUTO-TRIES (S2013/MIT- 2713). Also, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.

Proceeding of: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Hamburg, Germany, September 13-15, 2012. ; Road safety applications require the most reliable and trustable sensors. Context information plays also a key role, adding trustability and allowing the study of the interactions and the danger inherent to them. Vehicle dynamics, dimensions. can be very useful to avoid misdetections when performing vehicle detection and tracking (fusion levels 0 and 1). Traffic safety information is mandatory for fusion levels 2 and 3 by evaluating the interactions and the danger involved in any detection. All this information is context information that was used in this application to enhance the capacity of the sensors, providing a complete and multilevel fusion application. Present application use three sensors: laser scanner, computer vision and inertial system, the information given by these sensors is completed with context information, providing reliable vehicle detection and danger evaluation. Test results are provided to check the usability of the detection algorithm. ; This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03- 01), Driver Distraction Detector System (GRANT TRA2011-29454-C03-02). CAM through SEGVAUTO (S2009/DPI-1509). ; Publicado

Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on image processing. In this paper, a real time speed supervisor based on road sign recognition that can work both in urban and non-urban environments is presented. The system is able to recognize 135 road signs, belonging to the danger, yield, prohibition obligation and indication types, and sends warning messages to the driver upon the combination of two pieces of information: the current speed of the car and the road sign symbol. The core of this paper is the comparison between the two main methods which have been traditionally used for detection and recognition of road signs: template matching (TM) and neural networks (NN). The advantages and disadvantages of the two approaches will be shown and commented. Additionally we will show how the use of well-known algorithms to avoid illumination issues reduces the amount of images needed to train a neural network. ; The work reported in this article has been partly funded by the Spanish Government by the grants FEDORA (TRA2010-20225-C03-01) and D3System (TRA2011-29454-C03-02).

Proceeding of: 2010 IEEE Intelligent Vehicles Symposium, University of California, San Diego, CA, USA June 21-24, 2010 ; The lack of trustworthy sensors makes ADAS applications a tough task. In this paper a fusion approach that uses two sensors: far infrared camera vision and a laser scanner to detect pedestrian for ADAS applications is presented. Both sensors have different field of view thus different detection zones are created according to the number of sensors available. The proposed algorithm combines low and high level information, providing a hybrid fusion scheme. Experimental results show that pedestrian detections are improved thanks to the use of several sensors. ; This work also was supported by the Spanish Government through the CICYT projects VISVIA (Grant TRA2007- 67786-C02-02) and POCIMA (Grant TRA2007-67374-C02- 01) and CAM through SEGVAUTO-II. ; Publicado

The aim of this work is to integrate and analyze the performance of a path planning method based on Time Elastic Bands (TEB) in real research platform based on Ackermann model. Moreover, it will be proved that all modules related to the navigation can coexist and work together to achieve the goal point without any collision. The study is done by analyzing the trajectory generated from global and local planners. The software prototyping tool is Robot Operating System (ROS) from Open Source Robotics Foundation and the research platform is the iCab (Intelligent Campus Automobile) from University Carlos III. This work has been validated from a test inside the campus where the iCab has performed the navigation between the starting point and the goal point without any collision. During the experiment, we proved the low sensitivity of the TEB method to variations of the vehicle model configuration and constraints. ; This research is supported by Madrid Community Project SEGVAUTO-TRIES and by the Spanish Government CICYT Projects (TRA2013-48314-C3-1-R, TRA2015-63708-R, and TRA2016-78886-C3-1-R).

Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments. ; This work was supported by the Spanish Government through the CICYTprojects (TRA2013-48314-C3-1-R and TRA2015-63708-R) and Comunidad de Madrid through SEGVAUTO-TRIES (S2013/MIT-2713). ; Publicado