Robust unattended and stolen object detection by fusing simple algorithms

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. J. C. San Miguel, and J. M. Martínez, "Robust unattended and stolen object detection by fusing simple algorithms", in IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 2008. AVSS '08, 2008, p. 18 - 25In this paper a new approach for detecting unattended or stolen objects in surveillance video is proposed. It is based on the fusion of evidence provided by three simple detectors. As a first step, the moving regions in the scene are detected and tracked. Then, these regions are classified as static or dynamic objects and human or nonhuman objects. Finally, objects detected as static and nonhuman are analyzed with each detector. Data from these detectors are fused together to select the best detection hypotheses. Experimental results show that the fusion-based approach increases the detection reliability as compared to the detectors and performs considerably well across a variety of multiple scenarios operating at realtime.This work is supported by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S- 050/TIC-0223 - ProMultiDis-CM), by the Consejería de Educación of the Comunidad de Madrid and by the European Social Fund

Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques

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. Bayona, J. C. SanMiguel, and J. M. Martínez, "Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques" in Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance. AVSS 2009, p. 25 - 30In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary object detection approaches comparing them in typical surveillance scenarios (extracted from standard datasets). Firstly, the existing approaches based on background-subtraction are organized into categories. Then, a representative technique of each category is selected and described. Finally, a comparative evaluation using objective and subjective criteria is performed on video surveillance sequences selected from the PETS 2006 and i-LIDS for AVSS 2007 datasets, analyzing the advantages and drawbacks of each selected approach.This work has partially supported by the Cátedra UAMInfoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad"), the Spanish Administration agency CDTI (CENIT-VISION 2007-1007), by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S-050/TIC-0223- ProMultiDis), by the Consejería de Educación of the Comunidad de Madrid, and by The European Social Fund

A semantic-based probabilistic approach for real-time video event recognition

This is the author’s version of a work that was accepted for publication in Journal 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 Journal Computer Vision and Image Understanding, 116, 9 (2012) DOI: 10.1016/j.cviu.2012.04.005This paper presents an approach for real-time video event recognition that combines the accuracy and descriptive capabilities of, respectively, probabilistic and semantic approaches. Based on a state-of-art knowledge representation, we define a methodology for building recognition strategies from event descriptions that consider the uncertainty of the low-level analysis. Then, we efficiently organize such strategies for performing the recognition according to the temporal characteristics of events. In particular, we use Bayesian Networks and probabilistically-extended Petri Nets for recognizing, respectively, simple and complex events. For demonstrating the proposed approach, a framework has been implemented for recognizing human-object interactions in the video monitoring domain. The experimental results show that our approach improves the event recognition performance as compared to the widely used deterministic approach.This work has been partially supported by the Spanish Administration agency CDTI (CENIT-VISION 2007- 1007), by the Spanish Government (TEC2011-25995 EventVideo), by the Consejería de Educación of the Comunidad de Madrid and by The European Social Fund

Detección de objetos abandonados

La detección de objetos abandonados es una de las áreas de interés en el ámbito de visión por computador, cuyo fin es evitar situaciones catastróficas. Para solucionar este problema han surgido investigaciones que incluyen una etapa de seguimiento. El algoritmo que se expone aquí deja de lado esta etapa para darle paso a un sistema basado en un modelo de fondo dual adaptativo, con el fin de obtener una implementación con bajo costo computacional, que detecte objetos abandonados en espacios interiores.Ingeniero (a) ElectrónicoPregrad

Detección de anomalías en tiempo real

Este trabajo tiene como objetivo la integración de un algoritmo de detección de anomalías en tiempo real en una plataforma destinada al trabajo con aplicaciones de vídeo vigilancia denominada DiVA. Con esta integración se pretende modificar la funcionalidad del algoritmo para que el mismo capaz de procesar imágenes recibidas de videocámaras en tiempo real sin comprometer el resto de su funcionamiento. Por otro lado se quiere desarrollar una interfaz de gráfica que facilite el manejo de dicho algoritmo, tanto a usuarios cualificados en el manejo de algoritmos de tratamiento de vídeo, como a usuarios sin conocimientos previos del tema en cuestión. La interfaz deberá ajustarse a las necesidades propias del algoritmo y de los futuros usuarios. Para llevara a cabo dicho trabajo se ha necesitado obtener conocimientos de varios campos previamente a la implementación de cada una de sus partes. En primer lugar se ha estudiado el algoritmo dado para su integración y todos los aspectos relativos del mismo para su integración en la plataforma y el desarrollo de la interfaz adecuada al mismo. De igual manera se han estudiado las distintas plataformas y herramientas necesarias para el desarrollo del conjunto. Una vez completados los objetivos principales se pasó a realizar el estudio y evaluación de los resultados obtenidos mediante una serie de pruebas realizadas en entornos adecuados al funcionamiento de la aplicación. Esta memoria es el pretende ser el reflejo del trabajo llevado a cabo durante los meses de duración del mismo a la vez que intenta ayudar al lector en la compresión de cada uno de los elementos que componen dicho trabajo.This paper aims at integrating an anomaly detection algorithm real time on a platform designed to work with videosurveillance applications called DiVA. With this integration it intend to modify the functionality of the algorithm so that it can process images received from video in real time without compromising the rest of its operations. On the other hand it want to develop a GUI to make easier the management of this algorithm, both skilled user in handling video processing algorithms, and user without knowledge of the subject matter. The GUI shall adjust to the own needs of both the algorithm and the future users. To carry out this work, it's been necessary to obtain knowledge of several fields, before to start developing each of its parts. First of all, the algorithm has been studied and all of its parts for the integration. Same way, it's been studied all the different platforms and tools needed for the develop of the set. Once all the main objectives were completed it proceeded to study and evaluation of the results through a set of test performed in appropiate enviroments. This report is intended to be reflect of the work carried out during the duration months in same way it tries to help the reader in understanding of each ones of the elements of the work