Einstein constraints on a characteristic cone

We analyse the Cauchy problem on a characteristic cone, including its vertex, for the Einstein equations in arbitrary dimensions. We use a wave map gauge, solve the obtained constraints and show gauge conservation.Comment: 10 pages, to be published in the Proceedings of the 15th International Conference on Waves and Stability in Continuous Media, held in Palermo, 28th June to 1st July 200

A complete gauge-invariant formalism for arbitrary second-order perturbations of a Schwarzschild black hole

Using recently developed efficient symbolic manipulations tools, we present a general gauge-invariant formalism to study arbitrary radiative $(l\geq 2)$ second-order perturbations of a Schwarzschild black hole. In particular, we construct the second order Zerilli and Regge-Wheeler equations under the presence of any two first-order modes, reconstruct the perturbed metric in terms of the master scalars, and compute the radiated energy at null infinity. The results of this paper enable systematic studies of generic second order perturbations of the Schwarzschild spacetime. In particular, studies of mode-mode coupling and non-linear effects in gravitational radiation, the second-order stability of the Schwarzschild spacetime, or the geometry of the black hole horizon.Comment: 14 page

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)

Incorporating Wheelchair Users in People Detection

A wheelchair users detector is presented to extend people detection, providing a more general solution to detect people in environments such as houses adapted for independent and assisted living, hospitals, healthcare centers and senior residences. A wheelchair user model is incorporated in a detector whose detections are afterwards combined with the ones obtained using traditional people detectors (we define these as standing people detectors). We have trained a model for classical (DPM) and for modern (Faster-RCNN) detection algorithms, to compare their performance. Besides the extensibility proposed with respect to people detection, a dataset of video sequences has been recorded in a real in-door senior residence environment containing wheelchairs users and standing people and it has been released together with the associated groundtruthThis work has been partially supported by the Spanish government under the project TEC2014-53176-R (HAVideo) and by the Spanish Government FPU grant programme (Ministerio de Educación, Cultura y Deporte

High-order gauge-invariant perturbations of a spherical spacetime

We complete the formulation of a general framework for the analysis of high-order nonspherical perturbations of a four-dimensional spherical spacetime by including a gauge-invariant description of the perturbations. We present a general algorithm to construct these invariants and provide explicit formulas for the case of second-order metric perturbations. We show that the well-known problem of lack of invariance for the first-order perturbations with l=0,1 propagates to increasing values of l for perturbations of higher order, owing to mode coupling. We also discuss in which circumstances it is possible to construct the invariants