Range unit root tests

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of "long-wave" patterns observed not only in unit root time series but also in series following more complex data generating mechanism. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties. Among these properties are the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series

A range unit root test

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of long-wave patterns observed not only in unit root time series but also in series following more complex data generating mechanisms. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties, among which its error-model-free asymptotic distribution, the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series and is asymptotically immune to noise

La transgresión cenomanense en el sector septentrional de la "Serranía de Cuenca" (provincias de Cuenca y Guadalajara, Cordillera Ibérica)

Los materiales del Albense-Cenomanense que afloran al N. de la Serrania de Cuenca se encuentran delimitados entre dos discordancias, una previa al depósito de las arenas en Facies Utrillas y otra de edad Cenomanense superior. Estos materiales forman una sucesión con diez tramos litológicos diferentes, entre los que se indentifican, bastante reducidos de potencia, alguna de las unidades litoestratigráficas recientemente definidas en la Cordillera Ibérica. Estos tramos pueden agruparse en tres unidades litoestratigraficas con rango de Formación: Arenas de Utrillas, Dolomias y margas (sin denominación formal) y Calizas dolomiticas de Nuévalos-Dolomias Tableadas de Villa de ves. Desde el punto de vista evolutivo, estos materiales forman una transgresión compleja, con cinco secuencias  deposicionales separadas por discontinuidades. Por ultimo, se define la existencia de un elemento paleogeografico en la región de Peñalen-Taravilla, un escalón que delimita la extensión hacia el N. de la unidad secuencia deposicional basal

La transgresión cenomanense en el sector septentrional de la "Serranía de Cuenca" (provincias de Cuenca y Guadalajara, Cordillera Ibérica)

Los materiales del Albense-Cenomanense que afloran al N. de la Serrania de Cuenca se encuentran delimitados entre dos discordancias, una previa al depósito de las arenas en Facies Utrillas y otra de edad Cenomanense superior. Estos materiales forman una sucesión con diez tramos litológicos diferentes, entre los que se indentifican, bastante reducidos de potencia, alguna de las unidades litoestratigráficas recientemente definidas en la Cordillera Ibérica. Estos tramos pueden agruparse en tres unidades litoestratigraficas con rango de Formación: Arenas de Utrillas, Dolomias y margas (sin denominación formal) y Calizas dolomiticas de Nuévalos-Dolomias Tableadas de Villa de ves. Desde el punto de vista evolutivo, estos materiales forman una transgresión compleja, con cinco secuencias  deposicionales separadas por discontinuidades. Por ultimo, se define la existencia de un elemento paleogeografico en la región de Peñalen-Taravilla, un escalón que delimita la extensión hacia el N. de la unidad secuencia deposicional basal

Enhanced people detection combining appearance and motion information

This paper is a postprint of a paper submitted to and accepted for publication in Electronics Letters and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital LibraryThe combination of two of the most recent people detectors from the state of the art is proposed. It is already known that the combination of independent information sources is useful for any detection task. In relation with people detection, there are two main discriminative information sources that characterize a person: appearance and motion. We propose the combination of two recent approaches based on both information sources. Experimental results over an extensive dataset show that the proposed combination significantly improves the results.This work was partially supported by the Universidad Autónoma de Madrid (“FPI-UAM”) and by the Spanish Goverment (“TEC2011-25995 EventVideo”)

People detection in surveillance: Classification and evaluation

This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library and at IEEE Xplore.Nowadays, people detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. The main objective of this study is to give a comprehensive and extensive evaluation of the state of the art of people detection regardless of the final surveillance application. For this reason, first, the different processing tasks involved in the automatic people detection in video sequences have been defined, then a proper classification of the state of the art of people detection has been made according to the two most critical tasks, object detection and person model, that are needed in every detection approach. Finally, experiments have been performed on an extensive dataset with different approaches that completely cover the proposed classification and support the conclusions drawn from the state of the art.This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo)

Robust real time moving people detection in surveillance scenarios

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. García Martín, and J. M. Martínez, "Robust real time moving people detection in surveillance scenarios", in 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, p. 241 - 247In this paper an improved real time algorithm for detecting pedestrians in surveillance video is proposed. The algorithm is based on people appearance and defines a person model as the union of four models of body parts. Firstly, motion segmentation is performed to detect moving pixels. Then, moving regions are extracted and tracked. Finally, the detected moving objects are classified as human or nonhuman objects. In order to test and validate the algorithm, we have developed a dataset containing annotated surveillance sequences of different complexity levels focused on the pedestrians detection. Experimental results over this dataset show that our approach performs considerably well at real time and even better than other real and non-real time approaches from the state of art.This work has partially supported by the Cátedra UAMInfoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad") and by the Spanish Government (TEC2007-65400 SemanticVideo)

Post-processing approaches for improving people detection performance

This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, 133 (2015) DOI: 10.1016/j.cviu.2014.09.010People detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. We address one of the main problems of people detection in video sequences: every people detector from the state of the art must maintain a balance between the number of false detections and the number of missing pedestrians. This compromise limits the global detection results. In order to reduce or relax this limitation and improve the detection results, we evaluate two different post-processing subtasks. Firstly, we propose the use of people-background segmentation as a filtering stage in people detection. Then, we evaluate the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results. And, finally, we evaluate the successive application of both post-processing approaches. Experiments have been performed on two extensive datasets and using different people detectors from the state of the art: the results show the benefits achieved using the proposed post-processing techniques.This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo)