Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.

Este trabalho tem por objetivo propor um sistema computacional automático capaz de identificar, a partir de imagens de vídeos, o comportamento do tráfego veicular rodoviário. Todos os códigos gerados foram escritos em Python, com o uso da biblioteca OpenCV. O primeiro passo do sistema proposto foi remover o background do frame do vídeo. Para isso, foram testados três métodos disponíveis no OpenCV, com métricas baseadas em uma Matriz de Contingência. O MOG2 foi escolhido como melhor método, processando 64 FPS, com mais de 95% de taxa de exatidão. O segundo passo do sistema envolveu detectar, rastrear e agrupar features dos veículos em movimento. Para isso, foi usado o algoritmo de Shi-Tomasi, junto com funções de fluxo ótico para o rastreamento. No agrupamento, usou-se a distância entre os pixels e as velocidades relativas de cada feature. No passo final, foram extraídos tanto as informações microscópicas quanto as informações macroscópicas em arquivos de relatório. Os arquivos têm padrões definidos, salvos em CSV. Também foi gerado, em tempo de execução, um diagrama espaço-tempo. Desse diagrama, é possível extrair informações importantes para as operações de sistemas de transportes. A contagem e a velocidade dos veículos foram usadas para validar as informações extraídas, comparadas a métodos tradicionais de coletas. Na contagem, o erro médio em todos os vídeos foi de 12,8%. Na velocidade, o erro ficou em torno de 9,9%. / The objective of this research is to propose an automatic computational system capable to identify, based on video images, traffic behavior on highways. All written code was made using Python, with the OpenCV library. The first step of the proposed system is to subtract the background from the frame. We tested three different background subtraction methods, using a contingency table to extract performance metrics. We decided that MOG2 was the best method for this research, processing frames at 64 FPS and scoring more than 95% on accuracy rate. The second step of the system was to detect, track and group all moving vehicle features. We used Shi-Tomasi detection method with optical flow to track features. We grouped features with a mixture of distance between pixels and relative velocity. For the last step, the algorithm exported microscopic and macroscopic information on CSV files. The system also produced a space-time diagram at runtime, in which it was possible to extract important information to transportation system operators. To validate the information extracted, we compared vehicle counting and velocities with traditional extraction methods. The algorithm had a mean error rate of 12.8% on counting vehicles, while achieving 9.9% error rate in velocity.

As the number of surveillance cameras and mobile platforms with cameras increases, automated detection and tracking of objects on these systems gain importance. There are various tracking methods designed for stationary or moving cameras. For stationary cameras, correspondence based tracking methods along with background subtraction have various advantages such as enabling detection of object entry and exit in a scene. They also provide robust tracking when the camera is static. However, they fail when the camera is moving. Conversely, histogram based methods such as mean shift enables object tracking on moving camera cases. Though, with mean shift object&rsquo / s entry and exit cannot be detected automatically which means a new object&rsquo / s manual initialization is required. In this thesis, we propose a dual-mode object tracking system which combines the benefits of correspondence based tracking and mean shift tracking. For each frame, a reliability measure based on background update rate is calculated. Interquartile Range is used for finding outliers on this measure and camera movement is detected. If the camera is stationary, correspondence based tracking is used and when camera is moving, the system switches to the mean shift tracking mode until the reliability of correspondence based tracking is sufficient according to the reliability measure. The results demonstrate that, in stationary camera mode, new objects can be detected automatically by correspondence based tracking along with background subtraction. When the camera starts to move, generation of false objects by correspondence based tracking is prevented by switching to mean shift tracking mode and handing over the correct bounding boxes with a seamless operation which enables continuous tracking.

Orientador: Clésio Luis Tozzi / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-16T06:32:57Z (GMT). No. of bitstreams: 1 Freitas_GreiceMartinsde_M.pdf: 16453422 bytes, checksum: fa0ae64561fd346237c57310fb0d0073 (MD5) Previous issue date: 2010 / Resumo: A crescente utilização de câmeras de vídeo para o monitoramento de ambientes, auxiliando no controle de entrada, saída e trânsito de indivíduos ou veículos tem aumentado a busca por sistemas visando a automatização do processo de monitoramento por vídeos. Como requisitos para estes sistemas identificam-se o tratamento da entrada e saída de objetos na cena, variações na forma e movimentação dos alvos seguidos, interações entre os alvos como encontros e separações, variações na iluminação da cena e o tratamento de ruídos presentes no vídeo. O presente trabalho analisa e avalia as principais etapas de um sistema de rastreamento de múltiplos objetos através de uma câmera de vídeo fixa e propõe um sistema de rastreamento baseado em sistemas encontrados na literatura. O sistema proposto é composto de três fases: identificação do foreground através de técnicas de subtração de fundo; associação de objetos quadro a quadro através de métricas de cor, área e posição do centróide - com o auxílio da aplicação do filtro de Kalman - e, finalmente, classificação dos objetos a cada quadro segundo um sistema de gerenciamento de objetos. Com o objetivo de verificar a eficiência do sistema de rastreamento proposto, testes foram realizados utilizando vídeos das bases de dados PETS e CAVIAR. A etapa de subtração de fundo foi avaliada através da comparação do modelo Eigenbackground, utilizado no presente sistema, com o modelo Mistura de Gaussianas, modelo de subtração de fundo mais utilizado em sistemas de rastreamento. O sistema de gerenciamento de objeto foi avaliado por meio da classificação e contagem manual dos objetos a cada quadro do vídeo. Estes resultados foram comparados à saída do sistema de gerenciamento de objetos. Os resultados obtidos mostraram que o sistema de rastreamento proposto foi capaz de reconhecer e rastrear objetos em movimento em sequências de vídeos, lidando com oclusões e separações, mostrando adequabilidade para aplicação em sistemas de segurança em tempo real / Abstract: There are immediate needs for the use of video cameras in environment monitoring, which can be verified by the task of assisting the entrance, exit and transit registering of people or vehicles in a area. In this context, automated surveillance systems based on video images are increasingly gaining interest. As requisites for these systems, it can be identified the treatment of entrances and exits of objects on a scene, shape variation and movement of followed targets, interactions between targets (such as meetings and splits), lighting variations and video noises. This work analyses and evaluates the main steps of a multiple target tracking system through a fixed video camera and proposes a tracking system based on approaches found in the literature. The proposed system is composed of three steps: foreground identification through background subtraction techniques; object association through color, area and centroid position matching, by using the Kalman filter to estimate the object's position in the next frame, and, lastly, object classification according an object management system. In order to assess the efficiency of the proposed tracking system, tests were performed by using videos from PETS and CAVIAR datasets. The background subtraction step was evaluated by means of a comparison between the Eigenbackground model, used in the proposed tracking system, and the Mixture of Gaussians model, one of the most used background subtraction models. The object management system was evaluated through manual classification and counting of objects on each video frame. These results were compared with the output of the object management system. The obtained results showed that the proposed tracking system was able to recognize and track objects in movement on videos, as well as dealing with occlusions and separations, and, at the same time, encouraging future studies in order for its application on real time security systems / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

Orientador: Siome Klein Goldenstein / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-13T11:06:36Z (GMT). No. of bitstreams: 1 Hauagge_DanielCabrini_M.pdf: 29273846 bytes, checksum: d697300202f6081a1441b4748b9a711c (MD5) Previous issue date: 2008 / Resumo: Apresentamos nesta dissertação quatro algoritmos voltados para a classicação automatizada de frutas. A subtração de fundo baseada na distância de Mahalanobis. O rastreamento das frutas em uma esteira usando a subtração de fundo, casamento de padrões e fluxo óptico. A reconstrução tridimensional da fruta a partir de imagens dela na esteira, onde recuperamos a posição da câmera com relação a fruta usando fluxo óptico e uma estimativa grosseira do movimento da fruta. A forma da fruta é obtida a partir das silhuetas reprojetadas no espaço tridimensional usando duas abordagens diferentes. Finalmente, a localização do pedúnculo e cálice a partir do eixo de simetria da reconstrução tridimensional. Realizamos testes com os quatro algoritmos. Obtivemos bons resultados com os dois primeiros. Para a reconstrução tridimensional verificamos bons resultados para algumas etapas do processo (fluxo óptico, estimativa inicial e otimização não-linear do movimento de câmera). Resultados fracos foram obtidos para a reprojeção das silhuetas usando os dois métodos. Analisamos as causas dos erros e propomos métodos que poderiam ser usados para melhorá-los. Os resultados da localização do pedúnculo e cálice foram insatisfatórios mas acreditamos que melhorariam se obtivéssemos uma reconstrução mais precisa. Também criamos um sistema de captura que reproduz as condições dentro de um sistema comercial de classificação. Com este aparato construímos quatro grandes bases de dados com aproximadamente 3000 frutas, 35 imagens de cada uma, contendo quatro variedades de maçã. Outras 6 bases menores foram criadas. / Abstract: We present in this dissertation four algorithms targeted at the automated classification of fruits. Background subtraction based on Mahalanobis distance. Fruit tracking on a conveyor belt using background subtraction, pattern matching and optical flow. The 3D reconstruction of the fruit from its images on the conveyor belt, where we recover the camera position, with respect to the fruit, using optical flow and a rough estimate of fruit motion. The fruit's shape is recovered from the silhouette re-projected into 3D space using two different approaches. Finally, the location of the stem and calyx based on the symmetry axis of the 3D reconstruction. We also present the results of tests conducted with the four algorithms. We obtained good results with the first two. For the three-dimensional reconstruction we obtained good results with some of the intermediary steps (optical flow, initial estimate and nonlinear re_nement of camera motion). Poor results were obtained for the re-projection of the silhouette's, using two approaches. We analyze the causes of these difficulties and suggest approaches that could improve them. The localization of stem and calyx was compromised by the poor 3D reconstruction so we believe that it will improve once we address the problems with the reconstruction algorithm. We created an image capturing system that reproduces the conditions inside a commercial grading machine. With this device we acquired four big data sets with approximately 3000 apples, 35 images of each, comprising four varieties. Another 6 smaller data-sets were also created. / Mestrado / Visão Computacional / Mestre em Ciência da Computação

Este trabalho tem por objetivo propor um sistema computacional automático capaz de identificar, a partir de imagens de vídeos, o comportamento do tráfego veicular rodoviário. Todos os códigos gerados foram escritos em Python, com o uso da biblioteca OpenCV. O primeiro passo do sistema proposto foi remover o background do frame do vídeo. Para isso, foram testados três métodos disponíveis no OpenCV, com métricas baseadas em uma Matriz de Contingência. O MOG2 foi escolhido como melhor método, processando 64 FPS, com mais de 95% de taxa de exatidão. O segundo passo do sistema envolveu detectar, rastrear e agrupar features dos veículos em movimento. Para isso, foi usado o algoritmo de Shi-Tomasi, junto com funções de fluxo ótico para o rastreamento. No agrupamento, usou-se a distância entre os pixels e as velocidades relativas de cada feature. No passo final, foram extraídos tanto as informações microscópicas quanto as informações macroscópicas em arquivos de relatório. Os arquivos têm padrões definidos, salvos em CSV. Também foi gerado, em tempo de execução, um diagrama espaço-tempo. Desse diagrama, é possível extrair informações importantes para as operações de sistemas de transportes. A contagem e a velocidade dos veículos foram usadas para validar as informações extraídas, comparadas a métodos tradicionais de coletas. Na contagem, o erro médio em todos os vídeos foi de 12,8%. Na velocidade, o erro ficou em torno de 9,9%. / The objective of this research is to propose an automatic computational system capable to identify, based on video images, traffic behavior on highways. All written code was made using Python, with the OpenCV library. The first step of the proposed system is to subtract the background from the frame. We tested three different background subtraction methods, using a contingency table to extract performance metrics. We decided that MOG2 was the best method for this research, processing frames at 64 FPS and scoring more than 95% on accuracy rate. The second step of the system was to detect, track and group all moving vehicle features. We used Shi-Tomasi detection method with optical flow to track features. We grouped features with a mixture of distance between pixels and relative velocity. For the last step, the algorithm exported microscopic and macroscopic information on CSV files. The system also produced a space-time diagram at runtime, in which it was possible to extract important information to transportation system operators. To validate the information extracted, we compared vehicle counting and velocities with traditional extraction methods. The algorithm had a mean error rate of 12.8% on counting vehicles, while achieving 9.9% error rate in velocity.

This thesis dissert on traffic monitoring. There are couple of different methods of background extraction and four methods vehicle detection described here. Furthermore there is one method that describes vehicle counting. All of these methods was realized in Matlab where was created graphical user interface. One whole chapter is dedicated to process of practical realization. All methods are compared by set of testing videos. These videos are resulting in statistics which diagnoses about efficiency of single one method.

Das Thema dieser Arbeit ist die Entwicklung einer Background Subtraction und deren Verwendung in einer Gestürzten-Personen-Erkennung im Kontext eines Roboter Nachtwächters in einer Pflegeeinrichtung. Dazu wird der aktuelle technische Stand bei der Background Subtraction betrachtet. Im Anschluss daran wird basierend auf der Recherche und den Rahmenbedingungen die durch das Einsatzszenario gegeben sind ein Ansatz gewählt und umgesetzt. / The topic of this thesis is the development of a background subtraction and its use in a fallen person detection in the context of a robot night watchman in a care facility. For this purpose, the current technical status of background subtraction is considered. Subsequently, an approach is selected and implemented based on the research and the conditions given by the application scenario.

Detecting a malicious hacker intruding on a network system can be difficult. This challenge is made even more complex by the network activity generated by normal users and by the fact that it is impossible to know the hacker’s exact actions. Instead, the defender of the network system has to infer the hacker’s actions by statistics collected by the intrusion detection system, IDS. This thesis investigates the performance of hidden Markov models, HMM, to detect an intrusion automatically under different background activities generated by normal users. Furthermore, background subtraction techniques with inspiration from computer vision are investigated to see if normal users’ activity can be filtered out to improve the performance of the HMMs.The results suggest that the performance of HMMs are not sensitive to the type of background activity but rather to the number of normal users present. Furthermore, background subtraction enhances the performance of HMMs slightly. However, further investigations into how background subtraction performs when there are many normal users must be done before any definitive conclusions. / Det kan vara svårt att upptäcka en hackare som gör intrång i ett nätverkssystem. Utmaningen blir ännu större genom nätverksaktiviteten som genereras av vanliga användare och av det faktum att det är omöjligt att veta hackarens exakta handlingar. Istället måste nätverkssystemets försvarare använda insamlad data från intrångsdetekteringssystemet, IDS, för att estimera hackarens handlingar. Detta arbete undersöker förmågan hos dolda Markovmodeller, HMM, att automatiskt upptäcka dataintrång under olika typer av bakgrundsaktiviteter som genereras av normala användare. Dessutom undersöks bakgrundssubtraktionstekniker med inspiration från datorseende för att se om normala användares aktivitet kan filtreras bort för att förbättra prestanda hos HMM. Resultaten tyder på att prestandan för HMM inte är känsliga för typen av bakgrundsaktivitet utan snarare för antalet närvarande normala användare. Dessutom förbättrar bakgrundssubtraktion prestandan hos HMM. Det krävs dock mer forskning för att dra definitiva slutsatser kring vilken effekt bakgrundssubstitution har när antalet normala användare är stort.

In this study, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Background subtraction has been performed to detect the moving objects in the video, which has been taken from a static camera. Four methods, frame differencing, running (moving) average, eigenbackground subtraction and mixture of Gaussians, have been used in the background subtraction process. After background subtraction, using some additional operations, such as morphological operations and connected component analysis, the objects to be tracked have been acquired. While tracking the moving objects, active contour models (snakes) has been used as one of the approaches. In addition to this method / Kalman tracker and mean-shift tracker are other approaches which have been utilized. A new approach has been proposed for the problem of tracking multiple targets. We have implemented this method for single and multiple camera configurations. Multiple cameras have been used to augment the measurements. Homography matrix has been calculated to find the correspondence between cameras. Then, measurements and tracks have been associated by the new tracking method.