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)

Context-aware part-based people detection for video monitoring

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 IEEE Digital LibraryA novel approach for part-based people detection in images that uses contextual information is proposed. Two sources of context are distinguished regarding the local (neighbour) information and the relative importance of the parts in the model. Local context determines part visibility which is derived from the spatial location of static objects in the scene and from the relation between scales of analysis and detection window sizes. Experimental results over various datasets show that the proposed use of context outperforms the related state-of-the-art.This work was supported by the Spanish Government (HA-Video TEC2014-5317-R)

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

Contributions to robust people detection in video-surveillance

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: junio de 201

Aplicación de visión por computador basada en Sony Spresense

Esta memoria describe cómo se ha desarrollado una aplicación de visión por computador basada en la placa de desarrollo Sony Spresense. Toma como punto de referencia el estudio previo realizado por el estudiante Yeray Navarro en su Trabajo de Fin de Grado, Placa programable Spresense de Sony. Desarroyo de una aplicación de visión por computador" y los modelos de visión por computador definidos en él. A lo largo del trabajo se definen diversas etapas que engloban el desarrollo de la aplicación, desde las etapas iniciales donde se concretan los objetivos, hasta el uso práctico de la aplicación. El desarrollo de este trabajo se pretende aplicar a la mejora de la prótesis de brazo diseñada y fabricada por el equipo de ARM2u, proyecto universitario impulsado por la Escuela Técnica Superior de Ingeniería Industrial de Barcelona, ETSEIB, de la Universidad Politécnica de Cataluña, UPC. El proceso de creación de la aplicación se realiza de forma segmentada, empezando por un código base embebido en la placa de desarrollo de captura y clasificación de imágenes, pasando por una primera versión de la aplicación unida al código de movimiento de la prótesis y finalizando con una optimización de la misma. Al concluir este trabajo se presenta una aplicación embebida en la placa de desarrollo Sony Spresense, cuyas funcionalidades son procesadas completamente por el procesador incorporado en la placa. Los resultados de diferentes pruebas realizadas sobre la aplicación permiten demostrar su funcionalidad y validar el modelo de aplicación creado. Se configura como una aplicación que permite el movimiento natural de la prótesis pero, a voluntad del usuario, permite el cierre de la pinza al tamaño del objeto resultante de la clasificación de imágenesAquesta memòria descriu com s' ha desenvolupat una aplicació de visió per computador basada en la placa de desenvolupament Sony Spresense. Pren com a punt de referència l'estudi previ realitzat per l'estudiant Yeray Navarro en el seu Treball de Fi de Grau, "Placa Programable Spresense de Sony. Desarrollo de una aplicación de visión por computador" i els models de visió per computador definits en ell. Al llarg del treball es defineixen diverses etapes que engloben el desenvolupament de l'aplicació, des de les etapes inicials on es fixen els objectius, fins a l'ús pràctic de l'aplicació. El desenvolupament d' aquest treball va dirigit a la millora de la pròtesi de braç dissenyada i fabricada per l'equip ARM2u, projecte universitari impulsat per l'Escola Tècnica Superior d'Enginyeria Industrial de Barcelona, de la Universitat Politècnica de Catalunya, UPC. El procés de creació de l'aplicació es realitza de forma segmentada, començant per un codi base embegut a la placa de desenvolupament de captura i classificació d'imatges, passant per una primera versió de l'aplicació unida al codi de moviment de la pròtesi i finalitzant amb una optimització de la mateixa. En concloure aquest treball es presenta una aplicació embeguda a la placa de desenvolupament Sony Spresense, les funcionalitats de la qual són processades completament pel processador incorporat a la placa. Els resultats de diferents proves realitzades sobre l'aplicació permeten demostrar la seva funcionalitat i validar el model d' aplicació creat. Es configura com una aplicació que permet el moviment natural de la pròtesi però que, a voluntat de l'usuari, permet el tancament de la pinça a la mida de l' objecte resultant de la classificació d'imatgesThis report describes how a computer vision application based on the Sony Spresense development board has been developed. It takes as a point of reference the previous study carried out by the student Yeray Navarro in his Final Degree Project, "Placa Programable Spresense de Sony. Desarrollo de una aplicación de visión por computador" and the computer vision models defined in it. Throughout the work, various stages are defined that encompass the development of the application, from the initial stages where the objectives to be met are set to the practical use of the application. The development of this work is aimed at improving the arm prosthesis designed and manufactured by the ARM2u team, pushed forward by the Escola Tècnica Superior d'Enginyeria Industrial de Barcelona, ETSEIB, of the Universitat Politècnica de Catalunya, UPC. The process of creating the application is carried out in a segmented way, starting with a base code embedded in the image capture and classification development board, passing through a first version of the application together with the movement code of the prosthesis and ending with an optimization of it. At the conclusion of this work, an application embedded in the Sony Spresense development board is presented, whose functionalities are completely processed by the processor incorporated in the board. The results of different tests carried out on the application allow to demonstrate its functionality and validate the application model created. It is configured as an application that allows the natural movement of the prosthesis that, at the user's will, allows the closure of the clamp to the size of the object resulting from the classification of imag

A multi-configuration part-based person detector

Proceedings of the Special Session on Multimodal Security and Surveillance Analytics 2014, held during the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2014) in ViennaPeople detection is a task that has generated a great interest in the computer vision and specially in the surveillance community. One of the main problems of this task in crowded scenarios is the high number of occlusions deriving from persons appearing in groups. In this paper, we address this problem by combining individual body part detectors in a statistical driven way in order to be able to detect persons even in case of failure of any detection of the body parts, i.e., we propose a generic scheme to deal with partial occlusions. We demonstrate the validity of our approach and compare it with other state of the art approaches on several public datasets. In our experiments we consider sequences with different complexities in terms of occupation and therefore with different number of people present in the scene, in order to highlight the benefits and difficulties of the approaches considered for evaluation. The results show that our approach improves the results provided by state of the art approaches specially in the case of crowded scenesThis work has been done while visiting the Communication Systems Group at the Technische Universität Berlin (Germany) under the supervision of Prof. Dr.-Ing. Thomas Sikora. This work has been partially supported by the Universidad Aut´onoma de Madrid (“Programa propio de ayudas para estancias breves en España y extranjero para Personal Docente e Investigador en Formación de la UAM”), by the Spanish Government (TEC2011-25995 EventVideo) and by the European Community’s FP7 under grant agreement number 261776 (MOSAIC)

People-background segmentation with unequal error cost

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. Á. García-Martín, A. Cavallaro, J. M. Martínez, "People-background segmentation with unequal error cost", in 19th IEEE International Conference on Image Processing, ICIP 2012, p. 157 - 160We address the problem of segmenting a video in two classes of different semantic value, namely background and people, with the goal of guaranteeing that no people (or body parts) are classified as background. Body parts classified as background are given a higher classification error cost (segmentation with bias on background), as opposed to traditional approaches focused on people detection. To generate the people-background segmentation mask, the proposed approach first combines detection confidence maps of body parts and then extends them in order to derive a background mask, which is finally post-processed using morphological operators. Experiments validate the performance of our algorithm in different complex indoor and outdoor scenes with both static and moving cameras.Work partially supported by the Universidad Autónoma de Madrid (“FPI-UAM”) and by the Spanish Goverment (“TEC2011-25995 EventVideo”). This work was done while the first author was visting Queen Mary University of London