Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

Multiple object tracking using an automatic veriable-dimension particle filter

Object tracking through particle filtering has been widely addressed in recent years. However, most works assume a constant number of objects or utilize an external detector that monitors the entry or exit of objects in the scene. In this work, a novel tracking method based on particle filtering that is able to automatically track a variable number of objects is presented. As opposed to classical prior data assignment approaches, adaptation of tracks to the measurements is managed globally. Additionally, the designed particle filter is able to generate hypotheses on the presence of new objects in the scene, and to confirm or dismiss them by gradually adapting to the global observation. The method is especially suited for environments where traditional object detectors render noisy measurements and frequent artifacts, such as that given by a camera mounted on a vehicle, where it is proven to yield excellent results

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences

Aixecament arquitectònic i estudi constructiu de la masia l'Om de Pruit

El següent Projecte Final de Carrera s’ha dut a terme amb l’objectiu de complementar i ampliar la documentació vers les Masies de Catalunya que ja disposa el Taller de Patrimoni Arquitectònic de l’Escola Politècnica Superior d’Edificació de Barcelona. Concretament en aquest PFC s’ha realitzat un aixecament arquitectònic i estudi constructiu de la Masia l’Om de Pruit, situada a la subcomarca del Collsacabra (Osona). A més, alhora de realitzar aquest estudi també s’ha considerat convenientcontextualitzar el món de les masies en general i la realitat de la zona. A l’actualitat, està deshabitada i presenta un avançat estat de degradació. Així doncs, la Masia l’Om de Pruit es un clar exemple del procés de desaparició que està patint la masoveria catalana. A continuació, iniciarem l’estudi fent esment de les masies en general i el seu marc històric, per arribar posteriorment a ubicar la masia a la subcomarca de Collsacabra i realitzar l’aixecament constructiu. Una vegada realitzat l’estudi de la masia podem concloure que l’Om de Pruït és una d’aquestes masies orientades a l’antic ús agrícola i ramader i que donat els canvis sòcio-econòmics d’aquests últims anys que cada vegada estan més marcada per la pèrdua de l’activitat rural, ha fet que els propietaris no puguin afrontar el cost de la rehabilitació i això fa que l’estat de degradació de la masia vagi en augment. Gràcies a la realització d’aquest projecte de Final de Carrera, ens hem endinsat més en el mon rural i en la seva història, i això ha fet possible ampliar els nostres coneixements tant, històrics i constructius envers d’aquest món. També hem realitzat un aprenentatge bàsicament pràctic dels treballs de camp de topografia, que hem pogut dur a terme gràcies als coneixements bàsics adquirits durant la carrera

Robust Multiple Lane Road Modeling Based on Perspective Analysis

Road modeling is the first step towards environment perception within driver assistance video-based systems. Typically, lane modeling allows applications such as lane departure warning or lane invasion by other vehicles. In this paper, a new monocular image processing strategy that achieves a robust multiple lane model is proposed. The identification of multiple lanes is done by firstly detecting the own lane and estimating its geometry under perspective distortion. The perspective analysis and curve fitting allows to hypothesize adjacent lanes assuming some a priori knowledge about the road. The verification of these hypotheses is carried out by a confidence level analysis. Several types of sequences have been tested, with different illumination conditions, presence of shadows and significant curvature, all performing in realtime. Results show the robustness of the system, delivering accurate multiple lane road models in most situations

A Multi Camera and Multi Laser Calibration Method for 3D Reconstruction of Revolution Parts

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm

Field Fluctuations and Casimir Energy of 1D-Fermions

Producción CientíficaWe investigate the self-adjoint extensions of the Dirac operator of a massive one-dimensional field of mass m confined in a finite filament of length L. We compute the spectrum of vacuum fluctuations of the Dirac field under the most general dispersionless boundary conditions. We identify its edge states in the mass gap within a set of values of the boundary parameters, and compute the Casimir energy of the discrete normal modes. Two limit cases are considered, namely, that of light fermions with mL 1, and that of heavy fermions for which mL 1. It is found that both positive and negative energies are obtained for different sets of values of the boundary parameters. As a consequence of our calculation we demonstrate that the sign of the quantum vacuum energy is not fixed for exchange-symmetric plates (parity-invariant configurations), unlike for electromagnetic and scalar fields.Ministerio de Economía, Industria y Competitividad (grant MTM2014-57129-C2-1-P)Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (grants VA057U16 and BU229P18)Junta de Castilla y León (grant VA137G18

Multicolonization of human nasopharynx due to Neisseria spp.

The colonization due to Neisseria spp. in the nasopharynx of forty healthy adults was studied by using a selective medium that allows the differentiation of Neisseria species and inhibits the rest of pharyngeal microbiota. The medium detected a variety of colonial morphology types and some metabolic characteristics of the isolates. We demonstrated the multicolonization by several Neisseria spp. in the same individual, and we isolated several strains of the same species, after analysis by pulsed-field gel electrophoresis (PFGE) patterns obtained from the different colonial types previously identified as the same species. The forty adults studied were colonized by 112 forms of Neisseria spp., and twelve colonization patterns were obtained: one species (45%), two (45%), three (7.5%) and four (2.5%). N. perflava-N. sicca, either alone or in combination with other species was the most frequent isolate (92.5%). The analysis of PFGE patterns obtained from different colonial types revealed the multicolonization by several strains of the same species in some individuals. This fact was found in N. perflava-N. sicca (50%) and N. mucosa (2.5%)

Real-time robust estimation of vanishing points through nonlinear optimization

Vanishing points are elements of great interest in the computer vision field, since they are the main source of information about the geometry of the scene and the projection process associated to the camera. They have been studied and applied during decades for plane rectification, 3D reconstruction, and mainly auto-calibration tasks. Nevertheless, the literature lacks accurate online solutions for multiple vanishing point estimation. Most strategies focalize on the accuracy, using highly computational demanding iterative procedures. We propose a novel strategy for multiple vanishing point estimation that finds a trade-off between accuracy and efficiency, being able to operate in real time for video sequences. This strategy takes advantage of the temporal coherence of the images of the sequences to reduce the computational load of the processing algorithms while keeping a high level of accuracy due to an optimization process. The key element of the approach is a robust scheme based on the MLESAC algorithm, which is used in a similar way to the EM algorithm. This approach ensures robust and accurate estimations, since we use the MLESAC in combination with a novel error function, based on the angular error between the vanishing point and the image features. To increase the speed of the MLESAC algorithm, the selection of the minimal sample sets is substituted by a random sampling step that takes into account temporal information to provide better initializations. Besides, for the sake of flexibility, the proposed error function has been designed to work using as image features indiscriminately gradient-pixels or line segments. Hence, we increase the range of applications in which our approach can be used, according to the type of information that is available. The results show a real-time system that delivers real-time accurate estimations of multiple vanishing points for online processing, tested in moving camera video sequences of structured scenarios, both indoors and outdoors, such as rooms, corridors, facades, roads, etc

Non-linear optimization for robust estimation of vanishing points

A new method for robust estimation of vanishing points is introduced in this paper. It is based on the MSAC (M-estimator Sample and Consensus) algorithm and on the definition of a new distance function between a vanishing point and a given orientation. Apart from the robustness, our method represents a flexible and efficient solution, since it allows to work with different type of image data, and its iterative nature makes better use of the available information to obtain more accurate estimates. The key issue of the work is the proposed distance function, that makes the error to be independent from the position of an hypothesized vanishing point, which allows to work with points at the infinity. Besides, the estimation process is guided by a non-linear optimization process that enhances the accuracy of the system. The robustness of our proposal, compared with other methods in the literature is shown with a set of tests carried out for both synthetic data and real images. The results show that our approach obtain excellent levels of accuracy and that is definitely robust against the presence of large amounts of outliers, outperforming other state of the art approaches