Detection of unattended and stolen objects in videos

Abstract-This research work presents an efficient approach of detecting unattended or stolen objects in live videos based on background subtraction and foreground analysis. The most common algorithm for performing background subtraction is the Gaussian Mixture model (GMM). An improved Multi-Gaussian Adaptive background model is employed for background subtraction to determine the static region. A simple split and merge method is used to detect the static region from which the static objects are identified. The time and presence of static objects, which may be either unattended or stolen, are informed by sending a mail and SMS to the security officials. Also, Haralick's texture operators are employed for images to identify objects under low contrast situations. The system is efficient to run in real time and produce good results

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique

Estudio e implementación de algoritmos para detección de anomalías en entornos de videovigilancia

Este Trabajo Fin de Grado tiene como objetivo el estudio y desarrollo de distintas técnicas de análisis de secuencias de videovigilancia que permitan la detección de anomalías presentes en las mismas para, a partir de ello, construir un sistema completo que permita llevar a cabo esta tarea. Basándose en la literatura y otros trabajos sobre detección de anomalías en vídeo, se ha diseñado el detector, compuesto por diferentes etapas de procesamiento que se han ido analizando de manera independiente. De este diseño, donde más se ha profundizado ha sido en la elección y extracción de características, decidiéndose por las características espaciotemporales de los gradientes de intensidad para este trabajo. A partir de estas, se ha establecido un método de entrenamiento y aprendizaje y se han implementado y analizado dos algoritmos para modelar el comportamiento de las escenas, evolucionando desde un sistema basado en distancias para detectar las anomalías hasta uno basado en niveles de pertenencia a cada clase, que son K-Means y Fuzzy C-Means. Se analizan las ventajas e inconvenientes de cada uno, y se encuentra el ajuste óptimo de ambos a través de una serie de pruebas predefinidas para ello. El estudio de los resultados se realiza a través de secuencias de videovigilancia reales en diferentes entornos y situaciones, para todas las distintas secuencias utilizadas y para cada uno de los modelos propuestos, siguiendo de esta manera una configuración de lo particular a lo general motivado por la poca similitud existente entre cada una de las secuencias. Para concluir el trabajo, se verifica si se han alcanzado los objetivos marcados y se trata de extraer conclusiones sobre las características empleadas y los algoritmos implementados, así como establecer una serie de posibles mejoras futuras para el sistema.This Bachelor Thesis aims to study and develop different techniques for de analysis of video sequences in a surveillance environment in order to detect anomalies present in the scene, and therefore build a complete system to carry out this task. Based on the literature and other papers about anomaly detection in video, the detector has been designed from blocks, and every block has been analyzed separately. In this design, there has been deepened specially in the feature choice and extraction, being decided in this thesis for the spatio-temporal features of the intensity gradients. From these, it has been stablished a training and learning method, and two algorithms for modeling the scene behavior have been implemented and analyzed, evolving from a distance based system to detect anomalies until one based on the amount of membership to each class which are K-Means and Fuzzy C-Means. The advantages and disadvantages of each method are analyzed and on adequate adjustment is found throughout a series of predefined tests. The analysis of results is done with real video surveillance sequences from different environments and situations, for every sequence and every model proposed, thus following a configuration from particularity to generality, motivated by the small similarity between each of the sequences. In order to finish the thesis, it is verified if the marked objectives have been reached and it is tried to extract conclusions about the features and algorithms used and implemented, as well as propose some possible future improvements for the system

Kablosuz Çoklu Ortam Duyarga Ağlarında Gözetleme Uygulamaları için Füzyon-Tabanlı Çatı Tasarımı ve Geliştirilmesi

TÜBİTAK MFAG Proje15.07.2018Bu proje kapsamında, kablosuz çoklu ortam duyarga ağları için özellikle aşağıda verilen ikikonuda çözüm üreten bir yaklaşım ve çatı (framework) geliştirilmesi amaçlanmıştır:- Halen kullanılan ağlara göre daha az enerji tüketen bir kablosuz duyarga ağı kümelemealgoritmasının geliştirilmesi: Proje kapsamında yeni bir kümeleme algoritması geliştirilmiştir.Geliştirilen algoritma, gözetleme uygulamaları da dahil olmak üzere uygulamadan bağımsızve enerji-etkin çalışabilecek şekilde tasarlanmıştır. Geliştirilen algoritma, gerçek duyargadüğüm donanımları üzerinde de kolaylıkla çalışabilir nitelikte dağıtık ve hafif bir yapıdatasarlanmış eşit olmayan bir kümeleme yaklaşımı sergilemektedir. Tasarlanan kümelemealgoritması ile, çeşitli metotlarla konuşlandırılmış düğüm noktaları içeren kümelenmemiş birkablosuz duyarga ağdan, etkin olarak veri toplayabilecek kümelenmiş bir duyarga ağı eldeedilebilmektedir. Kümeleme için uygun parametreler belirlenmiş ve bulanık mantık tabanlı biralgoritma geliştirilmiştir. Kümeleme yarı çapı tespitinde ana istasyona uzaklık, düğüm noktasıkalan enerjisi ve düğüm noktası göreli bağlanabilirlik parametreleri, yönlendirme için ise linkortalama kalan enerjisi ve göreli uzaklık parametreleri algoritma içerisinde kullanılmıştır.- Ana istasyona taşınacak bilginin miktarını azaltırken doğruluk oranını artıracak yöntemleringeliştirilmesi: Duyarga düğümlerinden ana istasyona kadar üç seviyede değişik veri füzyonyöntemleri kullanarak nesne çıkarımı yapan ve bu sayede taşınan veri miktarını azaltarakduyarga ağın ömrünü uzatan bir yöntem geliştirilmiştir. Bu çerçevede, ilk seviyede PKÖ,sismik ve akustik duyargalardan elde edilen veriler kullanılmıştır. Söz konusu skalerduyargalardan gelen veriler füzyon işlemine sokularak duyarganın kontrol ettiği alanda insanve araç gibi bir nesnenin olup olmadığı konusunda ilk karar oluşturulmaktadır. Bu karara göreikinci seviyede çoklu ortam duyargalarının (kamera ve mikrofon) uyandırılmasıgerçekleştirilmektedir. Kamera tarafından alınan görüntü ve mikrofon tarafından alınan sesişlenerek nesne tespiti yapılmaktadır. İkinci seviye füzyonu kapsamında görüntü ve sestençıkarılan bilgiler bir füzyon işleminden geçirilerek nesne sınıflandırılması doğruluk oranıartırılmaktadır. Duyarga düğümü üzerinde gerçekleştirilen bu işlemlerin ardından üretilen özetbilgi ana istasyona iletilmektedir. Üçüncü seviye füzyon ve sınıflandırma işleminde farklıkiplerden elde edilen veriler ile kip içi ve kipler arası korelasyonlar da kullanılarak, dahagelişmiş bir tanıma işlemi gerçekleştirilmektedir. Bu işlem enerji ve kaynak kullanım maliyetigerektirdiği için ana istasyonda yapılmaktadır.Bu projenin özgün değeri, skaler duyargalara ilave olarak çoklu ortam duyargaları tarafındantoplanan görüntü ve ses verilerinin duyarga düğümü içerisinde işlenerek ve füzyon edilerekpotansiyel tehditlere yönelik anlamlı bilgiler üretilmesi ve bu sayede taşınacak verininboyutunun azaltılması ile taşınacak verinin ağ üzerinde daha etkin taşınmasını sağlayanözgün kümeleme algoritmasının geliştirilmesinde yatmaktadır.Proje öneri dokümanında yer alan planlı faaliyetlerin tamamı gerçekleştirilmiş ve projebaşlangıcında hedeflenen noktaya ulaşılmıştır. Proje kapsamında, 6 adet uluslararasıdergilerde (4 adet SCI-E, 1 adet SSCI, 1 adet ESCI indeksli) ve 9 adet konferanslarda(tamamı uluslararası konferans) olmak üzere toplam 15 adet yayın gerçekleştirilmiştir. Projekapsamında projenin değişik süreçlerinde görev alan 6 doktora ve 2 lisansüstü öğrencisinintez çalışmasına imkân sağlanmıştır (iki doktora tezi tamamlandı, altısı devam ediyor).Bu proje, BİLİMSEL VE TEKNOLOJİK ARAŞTIRMA PROJELERİNİ DESTEKLEMEPROGRAMI kapsamında TÜBİTAK tarafından 114R082 kod numarasıyla desteklenmiştir.In this project, a wireless sensor network clustering algorithm which consumes less energythan currently used networks and methods that increase the accuracy rate while reducingthe amount of information to be transferred to the base station have been studied. In thiscontext, a new distributed and lightweight fuzzy logic-based clustering algorithm withunequal clustering approach has been developed. In order to reduce the amount ofinformation to be transferred to the base station and to increase the accuracy, a methodextracting objects using data fusion methods at three different levels from sensor nodes tothe base station and reducing the amount of data carried in this way has been developed toextend the lifetime of a sensor network. At the first level, the data from scalar sensors arefused to decide whether or not there is an object in the controlled area. In the context of thesecond level fusion, information extracted from visual and audio data are fused to increaseobject classification accuracy. In the third level fusion and classification process performed inthe main station, a more advanced recognition process is performed using intra and intermode correlations between data obtained from different channels.The project has been terminated in 39 months with a three-months extension. In the project,five researchers, who are experts on multimedia applications, fuzzy logic and wirelesssensor networks, have been worked. An opportunity is provided for 6 PhD and 2 MSstudents, who have contributed to the project during different terms of the project, to work onand finish their thesis successfully. It is evaluated that the studies done in the project fill a biggap in the academic literature. During project, 6 journal papers and 9 internationalconference papers, which make 15 in total, are published

SegUA: um sistema móvel para apoio à rotina de vigilância

Mestrado em Engenharia de Computadores e TelemáticaOs serviços de vigilância e segurança têm a obrigação de documentar um conjunto de incidentes observados no decorrer das rondas, e esta documentação é, sobretudo, produzida em papel, o que dificulta a sua consolidação e processamento subsequentes. Para além disso, a vigilância é uma actividade inerentemente móvel, havendo, assim, espaço para estudar a aplicação de novos dispositivos computacionais no acompanhamento do profissional para facilitar a aquisição de dados, e organização e comunicação destes. Partindo da realidade do campus da Universidade de Aveiro e procedimentos associados, este trabalho procurou introduzir melhorias processuais nas actividades de vigilância e sua gestão. No novo processo de trabalho, os vigilantes utilizam um módulo móvel para navegar nos mapas de uma organização e registar, no ponto de observação, os incidentes, podendo extender o relato com elementos multimédia. Durante a execução das rondas, é possível, centralmente, observar o posicionamento dos Vigilantes e obter, de forma quase imediata, os resultados das rondas. Os incidentes são imediatamente encaminhados para os pontos de serviço da instituição, que têm a responsabilidade de tomar acções subsequentes, eliminando a necessidade de execução de um processo moroso de triagem actual. O sistema desenvolvido incorpora vistas orientadas por mapas, e integra-se com o Sistema de Informação Geográfica central para obter mapas e informações das entidades espaciais. ABSTRACT: Surveillance personnel need to report a wide range of incidents during their watches, which are mostly recorded on paper. This seriously hinders processing and archiving of such incidents for future analysis. The mobile nature of surveillance tasks presents an opportunity to study the introduction of mobile devices to assist in data acquisition, communication and organization, thus improving the quality of the data produced by the surveillance service. Using the Universidade de Aveiro and its associated surveillance procedures as a source of requirements, this project aims at improving the existing workflow and providing an incident handling system, thus enabling easy access to data. In the proposed workflow process, watchmen use a mobile device to report incidents, instead of paper. This mobile device allows the user to view maps and previously reported incidents, and to extend the incident facts with multimedia data. While watchmen carry out their watches, it is possible to centrally supervise their progress and current location in near real-time. In addition, reported incidents are immediately routed to their designated service points, where these incidents will be handled. The designed system supports map visualization, and integrates with the organization’s Geographic Information System to obtain spatial data and maps

AN ALGORITHM FOR RECONSTRUCTING THREE-DIMENSIONAL IMAGES FROM OVERLAPPING TWO-DIMENSIONAL INTENSITY MEASUREMENTS WITH RELAXED CAMERA POSITIONING REQUIREMENTS, WITH APPLICATION TO ADDITIVE MANUFACTURING

Cameras are everywhere for security purposes and there are often many cameras installed close to each other to cover areas of interest, such as airport passenger terminals. These systems are often designed to have overlapping fields of view to provide different aspects of the scene to review when, for example, law enforcement issues arise. However, these cameras are rarely, if ever positioned in a way that would be conducive to conventional stereo image processing. To address this, issue an algorithm was developed to rectify images measured under such conditions, and then perform stereo image reconstruction. The initial experiments described here were set up using two scientific cameras to capture overlapping images in various cameras positons. The results showed that the algorithm was accurately reconstructing the three-dimensional (3-D) surface locations of the input objects. During the research an opportunity arose to further develop and test the algorithms for the problem of monitoring the fabrication process inside a 3-D printer. The geometry of 3-D printers prevents the location of cameras in the conventional stereo imaging geometry, making the algorithms described above seem like an attractive solution to this problem. The emphasis in 3-D printing on using extremely low cost components and open source software, and the need to develop the means of comparing observed progress in the fabrication process to a model of the device being fabricated posed additional development challenges. Inside the 3-D printer the algorithm was applied using two scientific cameras to detect the errors during the printing of the low-cost open-source RepRap style 3-D printer developed by the Michigan Tech’s Open Sustainability Technology Lab. An algorithm to detect errors in the shape of a device being fabricated using only one camera was also developed. The results show that a 3-D reconstruction algorithm can be used to accurately detect the 3-D printing errors. The initial development of the algorithm was in MATLAB. The cost of the MATLAB software might prevent it from being used by open-source communities. Thus, the algorithm was ported to Python and made open-source for everyone to use and customize. To reduce the cost, the commonly used and widely available inexpensive webcams were also used instead of the expensive scientific cameras. In order to detect errors around the printed part, six webcams were used, so there were 3 pairs of webcams and each pair were 120 degrees apart. The results indicated that the algorithms are precisely detect the 3-D printing errors around the printed part in shape and size aspects. With this low-cost and open-source approach, the algorithms are ready for wide range of use and applications

An abandoned object detection system based on dual background segmentation

An abandoned object detection system is presented and evaluated using benchmark datasets. The detection is based on a simple mathematical model and works efficiently at QVGA resolution at which most CCTV cameras operate. The pre-processing involves a dual-time background subtraction algorithm which dynamically updates two sets of background, one after a very short interval (less than half a second) and the other after a relatively longer duration. The framework of the proposed algorithm is based on the Approximate Median model. An algorithm for tracking of abandoned objects even under occlusion is also proposed. Results show that the system is robust to variations in lighting conditions and the number of people in the scene. In addition, the system is simple and computationally less intensive as it avoids the use of expensive filters while achieving better detection results