Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments

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

Some empirical evidence on business-IT alignment processes in the public sector: A case study report

An empirical study that explores business-IT alignment processes in a networked organization among the province Overijssel, the municipalities Zwolle and Enschede, the water board district Regge & Dinkel and Royal Grolsch N.V. in The Netherlands, is summarized in this report. The aim of the study was to identify processes that contribute to improve such alignment. This study represents a continuation of previous validation efforts that help us to confirm the business-IT alignment process areas that should ultimately be included in the ICoNOs MM. Evidence was sought for the alignment of business and IT through the use of information systems to support the requirements of the organization in a specific project. The results of this study in the public sector also are relevant to the private sector where (i) business-IT alignment plays an increasingly valuable role, and (ii) the characteristics of collaborative networked organizations are present

Doing Good Today and Better Tomorrow: A Roadmap to High Impact Philanthropy Through Outcome-Focused Grantmaking

Describes Hewlett's experience with implementing the outcome-focused grantmaking (OFG) process in its environment program as a guide for identifying a portfolio of grants with maximum impact. Outlines trials and errors, recent innovations, and challenges

Projection Methods: Swiss Army Knives for Solving Feasibility and Best Approximation Problems with Halfspaces

We model a problem motivated by road design as a feasibility problem. Projections onto the constraint sets are obtained, and projection methods for solving the feasibility problem are studied. We present results of numerical experiments which demonstrate the efficacy of projection methods even for challenging nonconvex problems

Road geometry identification with mobile mapping techniques

Durante il mio dottorato mi sono occupato di Tecniche e Tecnologie innovative per la ricostruzione della geometria dei tracciati stradali esistenti, quali ad esempio Mobile Mapping, analisi immagini e dati GIS; a fronte degli elevatissimi costi oggi richiesti per l’utilizzo di veicoli strumentati già reperibili in commercio per il raggiungimento di tali scopi, il valore aggiunto del lavoro di dottorato riguarda l’uso di strumenti a basso costo che comportano un rilevante lavoro di analisi, trattamento e correzione del dato che risente in maniera decisiva della medio/bassa qualità della strumentazione in uso. L’obiettivo della ricerca è consistito nella realizzazione di un algoritmo di riconoscimento (in ambiente MATLAB) che sia in grado di restituire la geometria as-built di una strada esistente. Parte del lavoro è stata svolta nell’analisi e nell’estrazione delle curvature locali con approcci differenti (successive circonferenze locali, funzioni polinomiali di fitting locale di vario grado e con ampiezza di analisi variabile), nonché sullo studio degli angoli di deviazione locali. Usando questi parametri, nel resto del lavoro, si è prima ricercata una metodologia d’identificazione dei diversi elementi che compongono la geometria stradale, e poi si è lavorato su procedure di fitting con svariate tecniche (minimi quadrati, metodi robusti e altri algoritmi) cercando di estrarre informazioni di carattere geometrico, quali raggi di curvatura e relativi centri, lunghezza e orientamento dei rettifili, fattori di scala delle curve di transizione