Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

Background subtraction is the classical approach to differentiate moving objects in a scene from the static background when the camera is fixed. If the fixed camera assumption does not hold, a frame registration step is followed by the background subtraction. However, this registration step cannot perfectly compensate camera motion, thus errors like translations of pixels from their true registered position occur. In this paper, we overcome these errors with a simple, but effective background subtraction algorithm that combines Temporal and Spatio-Temporal approaches. The former models the temporal intensity distribution of each individual pixel. The latter classifies foreground and background pixels, taking into account the intensity distribution of each pixels' neighborhood. The experimental results show that our algorithm outperforms the state-of-the-art systems in the presence of jitter, in spite of its simplicity

Backward-Simulation Particle Smoother with a hybrid state for 3D vehicle trajectory, class and dimension simultaneous estimation

The estimation of the 3D trajectory, the class and the dimensions of a vehicle represents three relevant tasks for traffic monitoring. They are usually performed by separate sub-systems and only few existing algorithms cope with the three tasks at the same time. However, if these tasks are integrated, the trajectory estimation enforces the classification with temporal consistency, and at the same time, the estimation of the vehicle class and dimensions can be used to increase the trajectory estimate accuracy. In this work, we propose an algorithm to estimate the 3D trajectory, the class and the dimensions of vehicles simultaneously by means of a Backward-Simulation Particle Smoother whose state contains both continuous (vehicle pose and dimensions), and discrete (vehicle class) quantities. To integrate the class estimate in the Particle Smoother we model the class prediction as a Markov Chain. We performed experimental tests on both simulated and real datasets; they show that the pose and dimension estimation reaches centimeter-accuracy and the classification accuracy is higher than 95

An effective 6DoF motion model for 3D-6DoF Monte Carlo Localization

This paper deals with the probabilistic 6DoF motion model of a wheeled road vehicle. It allows to correctly model the error introduced by dead reckoning. Furthermore, to stress the importance of an appropriate motion model, i.e., that different models are not equally good, we show that another model, which was previously developed, does not allow a correct representation of the uncertainty, therefore misguiding 3D-6DoF Monte Carlo Localization. We also present some field experiments to demonstrate that our model allow a consistent determination of the 6DoF vehicle pose

O cultivo da macieira na Itália: porta-enxertos, cultivares, adubação e irrigação

A Itália é um dos principais países produtores de maçãs na Europa, destinada principalmente ao consumo in natura no mercado nacional e internacional. A produção e a qualidade dos frutos é dependente da cultivar, do porta-enxerto e das práticas de manejo, como a adubação e a irrigação adotado no pomar. Esta revisão tem por objetivo reportar as principais cultivares e porta-enxertos de macieira e o manejo da adubação e irrigação e suas atualizações em pomares de macieira da Itália. Os programas de melhoramento genético nesse país envolveram a seleção de cultivares e porta-enxertos de macieira que permitem a obtenção de altas produtividades e frutos de qualidade exigida pelo mercado consumidor. No manejo da adubação e irrigação, os nutrientes e a água têm sido fornecidos em quantidades próximas a real necessidade das plantas, proporcionando nutrição adequada, produção satisfatória e frutos de boa qualidade, além de evitar, sempre que possível, as perdas de nutrientes e água no ambiente

D.: Getting the most from your color camera in a color-coded world. RoboCup 2004: Robot Soccer World Cup VIII

Abstract. In this paper we present a proposal for setting camera parameters which we claim to give results better matched to applications in color-coded environments then the camera internal algorithms. Moreover it does not require online human intervention, i.e. is automated, and is faster than a human operator. This work applies to situations where the camera is used to extract information from a color-coded world. The experimental activity presented has been performed in the framework of Robocup mid-size rules, with the hypothesis of temporal constancy of light conditions; this work is the necessary first step toward dealing with slow changes, in the time domain, of light conditions

Evidence Accumulation Method for Mobile Robot Localization

We present a mobile robot localization method for known 2D environments. It is an evidence accumulation method where the complexity is reduced by means of a multiresolution scheme. The added value of the contribution, in the authors opinion, are 1) the method per s e; 2) the capability of the system to accept both raw sensor data as well as independently generated localization estimates; 3) the capability of the system to be both a global or a local localization system, depending on the availability of a global estimate; 4) the capability of the system to give out a (less) accurate estimate whenever asked to do so (e.g. before its regular completion), which could be called any-time localization