This work presents a new people re-identification method, using depth and intensity images, both of them captured with a single static camera, located in an overhead position. The proposed solution arises from the need that exists in many areas of application to carry out identification and re-identification processes to determine, for example, the time that people remain in a certain space, while fulfilling the requirement of preserving people’s privacy. This work is a novelty compared to other previous solutions, since the use of top-view images of depth and intensity allows obtaining information to perform the functions of identification and re-identification of people, maintaining their privacy and reducing occlusions. In the procedure of people identification and re-identification, only three frames of intensity and depth are used, so that the first one is obtained when the person enters the scene (frontal view), the second when it is in the central area of the scene (overhead view) and the third one when it leaves the scene (back view). In the implemented method only information from the head and shoulders of people with these three different perspectives is used. From these views three feature vectors are obtained in a simple way, two of them related to depth information and the other one related to intensity data. This increases the robustness of the method against lighting changes. The proposal has been evaluated in two different datasets and compared to other state-of-the-art proposal. The obtained results show a 96,7% success rate in re-identification, with sensors that use different operating principles, all of them obtaining depth and intensity information. Furthermore, the implemented method can work in real time on a PC, without using a GPU.

This paper describes the design, measurement methodology, and experimental validation of an electronic system for monitoring the electrical discontinuity in rails of double-track railway lines in order to detect the rail breakage. The electronic system is based on an emitter node and on the two receiver nodes. The three nodes, together with the four rails to be analyzed, constitute the electrical circuits, whose currents are measured in both the emission and reception nodes. These nodes can communicate through a data network between them, as well as with a remote center that controls and synchronizes the electronic systems along the railway line. The electronic subsystem associated with the emitter node transmits an encoded signal to the rails, modulated by an 800-Hz carrier. A set of eight currents are measured, one per rail at each end of the section defined between the emitter and the receiver, with a maximum length of 7 km. The evaluation of electrical discontinuities in each section is carried out by means of the relative imbalances that can appear between the eight measured currents, according to possible breakages. The method of measurement is characterized by a high immunity to noise. The results presented here are based on a railway line prototype, which implements the electrical propagation models and coupling between the four rails in the double track. This model allows the proposed electronic system and measurement methodology to be successfully verified.

This paper presents an algorithm that automatically corrects the distortion caused by multipath interference (MpI) in depth measurements obtained with time of flight cameras (ToF cameras). A radiometric model that explains, under some mild simplifications, the working principle of a ToF camera including a model for MpI is proposed. Using this model we demonstrate that all the information needed for compensating the influence of MpI on the scene captured by the camera is self-contained in the measurements (depth and amplitude of infrared signal). We propose an iterative optimization method that, based on the measurements contaminated with MpI, gives depth correction for each pixel. Results are shown in artificially generated time of flight scenes using the radiometric model. In addition, the system has been validated in real scenes using a commercial ToF camera providing good results.

The current stage of railway transportation systems must deal with increased safety and reliability issues. A key point is improving occupancy track circuit performance and providing them with redundancy, higher noise immunity, and the capability to acquire additional information about the track section involved. This work proposes a novel track circuit based on the encoding of the electrical transmissions with Kasami codes. Track circuit emitters send signals coded with a known sequence that can be identified by the corresponding receivers using correlation techniques; these processes increase immunity to noise and changes in environmental conditions. An appropriate selection of orthogonal sequences for encoding, as well as different carrier frequencies for transmissions, allow simultaneous emissions and receptions without cross interference.

On current railway systems, it is becoming ever more necessary to install safety elements to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles on the tracks, either fixed or mobile. In this paper, a multisensory system that can inform the monitoring system about the existence of obstacles is proposed. The system for obstacle detection consists of two emitting and receiving barriers, which are placed on opposing sides of the railway, respectively, and use infrared and ultrasonic sensors, thus establishing different optical and acoustic links between them. The interruption of one or several links should produce an alarm. However, even without the existence of objects, degradation of links could occur due to atmospheric attenuation, solar radiation, etc., also producing an activation of the alarm system. Since detection is based on the lack of radiation in the detectors, the use of complementary sensors for the same task is justified. Since the minimum size of an object for which an alarm is required to be generated is 50 × 50 × 50 cm, in some situations, several links are interrupted; however, alarms should not be generated. Typical cases are the flight of leaves or the movement of small animals in the scanned area. To avoid alarm activation in such situations, this paper proposes the combined use of diverse techniques of data fusion, based on fuzzy logic and the Dempster-Shafer theory of evidence, to validate the existence of objects, providing a highly reliable detection system.

The current development of railway transport gives a significant importance to the design of suitable and reliable safety systems. Among them, the common track circuit can be improved to enhance its performance, by providing redundancy, immunity to noise and more information from the track circuit. This work proposes a novel track circuit based on the encoding of the electrical transmissions with Kasami codes. Every node emits its corresponding code that can be detected at next nodes by correlation techniques. The suitable selection of orthogonal sequences, as well as different carrier frequencies for emissions, allow to have simultaneous emissions and receptions without cross interferences.

In this paper a ring of calibrated and synchronized cameras is used for achieving robot and obstacle localization inside a common observed area. To avoid complex appearance matching derived from the wide-baseline arrangement of cameras, a metric occupancy grid is obtained by intersection of silhouettes projected onto the floor. A particle filter is proposed for tracking multiple objects by using the grid as observation data. A clustering algorithm is included in the filter to increase the robustness and adaptability of the multimodal estimation task. To preserve identity of the robot from the set of tracked objects, odometry readings are used to compute a maximum likelihood (ML) global trajectory identification. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a human-cluttered scene.

A hardware solution is presented to obtain the eigenvalues and eigenvectors of a real and symmetrical matrix using field-programmable gate arrays (FPGAs). Currently, this system is used to compute the eigenvalues and eigenvectors in covariance matrices for applications in digital image processing that make use of the principal component analysis (PCA) technique. The proposed solution in this paper is based on the Jacobi method, but in comparison with other related works, it presents a different architecture that remarkably improves execution time, while reducing the number of consumed resources of the FPGA.

In this work, the use of the Generalized Principal Components Analysis (G-PCA) to improve the segmentation of moving objects in image sequences is proposed. In order to obtain this improvement, the noise components in the image derivatives are reduced, and afterwards, a method based on linear algebra is used to make the segmentation. Furthermore this work presents diverse tests to compare the results reached with and without the noise reduction in the image derivatives, and using the nonlinear minimization of an error function. A remarkable improvement in the segmentation quality and the processing time can be observed in every experiment when using the proposed method.

This work proposes a data fusion technique to validate the detection of objects in railway environments. The system for obstacle detection is based on an infrared emitting barrier and another of receivers placed on opposing side in the railway, establishing different optical links between them. The interruption of one or several links in the sensorial system should produce an alarm. Since the detection is based on the radiation lack in the detectors, channel degradation can produce a similar situation as if there was one obstacle. Since the interruption of the links is the necessary condition, but not sufficient, for the existence of an object, this work proposes the fusion of the sensors information by means of the Dempster-Shafer evidential theory to obtain a certainty value of the existence of objects supposing a risk for the railway.

The determination of the relative position among mobile objects or members of a robot team is a useful information when the systems are not restricted to a particular environment and they cannot depend on an external infrastructure to obtain their location information. The solution for this kind of problems cannot be divided and treated separately for each object. Therefore it is necessary to implement mechanisms that allow to simultaneously determine the spatial relations among objects not knowing their absolute location in the environment. After that, a positioning algorithm can be computed whose complexity depends on the number of observations made and the precision required in the system. In this work, the implementation of a relative positioning system is described where only acoustic emissions are made in order to obtain information about the other objects. These data are obtained by using simultaneous round-trip-time-of-flight measurements. Finally, using this information, the object positions are computed with a multidimensional scaling technique, and a closer solution is achieved with least-square algorithms.

In this work a processing algorithm is presented to obtain the impulse response of the transmission channel in a MIMO ultrasonic system. In this algorithm the macro-sequences (Ms) obtained from complementary sets of M sequences are used to obtain the impulse response of the environment. The signals received by the sensorial system are demodulated and correlated to find the transmitted signals. It is possible to obtain more detailed information from the environment through the analysis carried out over the correlated signal to extract the impulse response of the environment. In order to eliminate the cross correlation among the Ms and improve the localization of the correlation peaks, a post-process algorithm is applied to the correlated signal to find them with more accurately. Finally, a matrix of impulse responses of the transmission channels can be recovered by using the correlation peaks.

This work shows a modular architecture based on FPGA's to solve the eigenvalue problem according to the Jacobi method. This method is able to solve the eigenvalues and eigenvectors concurrently. The main contribution of this work is the low execution time compared with other sequential algorithms, and minimal internal FPGA consumed resources, mainly due to the fact of using the CORDIC algorithm. Two CORDIC modules have been designed to solve the trigonometric operations involved. A parallel CORDIC architecture is proposed as it is the best option to compute the eigenvalues with this method. Both CORDIC modules can work in rotation and vector mode. The whole system has been done in VHDL language, attempting to optimize the design.

This paper presents a reflector recognition and localization technique in three-dimensional (3-D) environments, using only times-of-flight (TOFs) data obtained from ultrasonic transducers. The recognition and localization technique is based on the principal component analysis applied to the TOF vectors originating from a sensor that contains two emitting transducers and several receivers. The two emitters simultaneously transmit two coded pulses that are detected later on and discriminated by the receivers, after being reflected in the environment. The proposed technique allows for the possibility of not only recognizing the reflectors, but also estimating approximately its localization referred to the sensor. This technique has been tested with three types of reflectors in 3-D environments: planes, edges, and corners. The achieved results are very satisfactory for reflectors located in the range 50-350 cm.

The detection of the passage of railway units by certain points of the railway line (mainly in the junctions or bifurcations of the routes), of reliable way, is a functionality of great importance for the safe management of the circulation. Most of the existing systems to that end bases of its operation on the detection of the disturbance that undergoes a magnetic flux (generated by means of coils) to the passage of the wheel of the train. With the system proposed here, we make this detection with the coils located next to the rail of the route, but without additional electronics located in this place. All the electronic systems are concentrated in a point that can be to a considerable distance from the rail. This makes easier the tasks of maintenance of the equipment, in addition to protecting it from the noises and aggressions of the environment. In order to compensate the attenuation of signal in the wires and the external noise, we propose a codification of the signal sent to the emitting coil by means of pairs of Golay complementary sequences.

Recent sonar modules applied to autonomous robots usually consist of several transducers, associated geometrically and electronically to improve performances and results, compared to systems based on only one isolated transducer. Nevertheless, in order to take advantage of these associations, it is necessary to develop new algorithms to consider suitably all the information given by times-of-flight (TOFs) from transducers. This work describes a new method to analyze these TOFs, so they can be used to obtain a heuristic map of the environment. A new operation mode is also proposed to allow the system adaptation to different environmental conditions in run-time.

This paper presents an ultrasonic sensor made up of several transducers located on various different horizontal planes, designed for navigation and localization applications in the mobile robot (MR) field. The sensor proposed is equipped with two emitters and fourteen receivers. The times-of-flight (OTF) provided by the transducers enable the tree types of basic reflectors (planes, corners and edges) to be classified. Optimal classification of the reflectors will facilitate both localization of the mobile robot and the generation of environment maps that are useful for navigation tasks. The innovation with this sensor as regards prior papers in this field lies in the high degree of redundancy that it provides, which greatly facilitates the detection of erroneous measurements caused by false reflections, and in the simplicity of the classification algorithm used, which gives it a low computational load.

This paper presents the development of a probabilistic algorithm based on an Extended Kalman Filter (EKF), used to estimate the absolute position of an indoor autonomous robot. With EKF it is possible to fuse relative and absolute positioning data, including some kind of uncertainty related to sensory systems. To reach this objective it is necessary to do an important model analysis to enable the on-line adaptation of the estimation algorithm. The development presented in this paper has been designed for an autonomous wheelchair, whose real-time and reliability constraints have to be taken into account in the algorithm.

Presents a method to control and guide mobile robots. In this case, to send different commands we have used electrooculography (EOG) techniques, so that, control is made by means of the ocular position (eye displacement into its orbit). A neural network is used to identify the inverse eye model, therefore the saccadic eye movements can be detected and where the user is looking can be determined. This control technique can be useful in multiple applications, but in this work it is used to guide an autonomous robot (wheelchair) as a system to help to people with severe disabilities. The system consists of a standard electric wheelchair with an on-board computer, sensors and graphical user interface running on a computer.

Describes a vision-based commands generation system, by face movements, applied to the guidance of an electric wheelchair for handicapped people with severe disabilities. Using a 2D color face tracker and a fuzzy detector the system computes face movements of the user and, depending on them, some commands are generated to drive the wheelchair. The system is non-intrusive and it allows visibility and freedom of head movements. It is able to learn the face movements of the user in an automatic initial setup, working even for people of different races. It is adaptive and, therefore, robust to light and background changes in inside environments. We report on some experimental results of this kind of guidance and some conclusions about its performance.

Nowadays, the digital register process of electrocardiographical signals (ECG) constitutes a common practice for the diagnosis and controlling of patients suffering from cardiac disorders. In this paper we study the usefulness of Artificial Neural Network (ANN) for clinical diagnosis through the detection of arrythmias and the reduction of the large spaces occupied by ECG records.