8 research outputs found
Summarizing the performances of a background subtraction algorithm measured on several videos
There exist many background subtraction algorithms to detect motion in
videos. To help comparing them, datasets with ground-truth data such as CDNET
or LASIESTA have been proposed. These datasets organize videos in categories
that represent typical challenges for background subtraction. The evaluation
procedure promoted by their authors consists in measuring performance
indicators for each video separately and to average them hierarchically, within
a category first, then between categories, a procedure which we name
"summarization". While the summarization by averaging performance indicators is
a valuable effort to standardize the evaluation procedure, it has no
theoretical justification and it breaks the intrinsic relationships between
summarized indicators. This leads to interpretation inconsistencies. In this
paper, we present a theoretical approach to summarize the performances for
multiple videos that preserves the relationships between performance
indicators. In addition, we give formulas and an algorithm to calculate
summarized performances. Finally, we showcase our observations on CDNET 2014.Comment: Copyright 2020 IEEE. Personal use of this material is permitted.
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Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods
This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read
Proposta de um Sistema de Tomada de DecisĂŁo para Detecção de VeĂculos em Movimento para FPGA
Os mĂ©todos pesquisados para detecção de objetos em movimento atravĂ©s do processamento de imagens em processadores de uso geral (General Purpose Processors - GPPs) apresentam, em sua maioria, uma abordagem que nĂŁo permite uma implementação com bons resultados em matriz de portas programável em campo (Field Programmable Gate Array-FPGA). Isso ocorre devido Ă classificação correta dos pixels estar diretamente relacionada Ă implementação de tĂ©cnicas mais complexas para modelar a imagem de referĂŞncia e que requerem muitos recursos em termos de memĂłria. AlĂ©m disso, quase todos os mĂ©todos analisados realizam apenas o processamento da tomada de decisĂŁo clássica, sendo poucas as propostas que baseiam sua tomada de decisĂŁo na integral fuzzy. Assim, visando melhorar a classificação dos pixels durante o processo de detecção de veĂculos em movimento Ă© proposta uma abordagem que realiza a fusĂŁo das tomadas de decisĂŁo fuzzy e clássica combinando tĂ©cnicas convencionais de processamento digital de imagens. Dessa forma, o sistema de tomada de decisĂŁo proposto para detectar os veĂculos em movimento busca nĂŁo comprometer os resultados em termos de classificação dos pixels mesmo utilizando um a tĂ©cnica de modelagem simples para obter a imagem de referĂŞncia. Essa imagem Ă© obtida atravĂ©s da estimativa do valor mediano e possibilita que o sistema de detecção de veĂculos em movimento proposto nĂŁo precise do armazenamento de várias imagens para obter a imagem de referĂŞncia. Os resultados sĂŁo verificados em termos de recursos ocupados, frequĂŞncia máxima de operação e classificação dos pixels em FPGAs de baixo custo. AlĂ©m disso, os resultados em termos de classificação dos pixels sĂŁo comparados atravĂ©s de várias medidas com outros mĂ©todos, apresentando resultados promissores no processamento de imagens em tempo real em FPGAs de baixo custo
Segmentation mutuelle d'objets d'intérêt dans des séquences d'images stéréo multispectrales
Les systèmes de vidéosurveillance automatisés actuellement déployés dans le monde sont encore bien loin de ceux qui sont représentés depuis des années dans les oeuvres de sciencefiction. Une des raisons derrière ce retard de développement est le manque d’outils de bas niveau permettant de traiter les données brutes captées sur le terrain. Le pré-traitement de ces données sert à réduire la quantité d’information qui transige vers des serveurs centralisés, qui eux effectuent l’interprétation complète du contenu visuel capté. L’identification d’objets d’intérêt dans les images brutes à partir de leur mouvement est un exemple de pré-traitement qui peut être réalisé. Toutefois, dans un contexte de vidéosurveillance, une méthode de pré-traitement ne peut généralement pas se fier à un modèle d’apparence ou de forme qui caractérise ces objets, car leur nature exacte n’est pas connue d’avance. Cela complique donc l’élaboration des méthodes de traitement de bas niveau.
Dans cette thèse, nous présentons différentes méthodes permettant de détecter et de segmenter des objets d’intérêt à partir de séquences vidéo de manière complètement automatisée. Nous explorons d’abord les approches de segmentation vidéo monoculaire par soustraction d’arrière-plan. Ces approches se basent sur l’idée que l’arrière-plan d’une scène peut être modélisé au fil du temps, et que toute variation importante d’apparence non prédite par le modèle dévoile en fait la présence d’un objet en intrusion. Le principal défi devant être relevé par ce type de méthode est que leur modèle d’arrière-plan doit pouvoir s’adapter aux changements dynamiques des conditions d’observation de la scène. La méthode conçue doit aussi pouvoir rester sensible à l’apparition de nouveaux objets d’intérêt, malgré cette robustesse accrue aux comportements dynamiques prévisibles. Nous proposons deux méthodes introduisant différentes techniques de modélisation qui permettent de mieux caractériser l’apparence de l’arrière-plan sans que le modèle soit affecté par les changements d’illumination, et qui analysent la persistance locale de l’arrière-plan afin de mieux détecter les objets d’intérêt temporairement immobilisés. Nous introduisons aussi de nouveaux mécanismes de rétroaction servant à ajuster les hyperparamètres de nos méthodes en fonction du dynamisme observé de la scène et de la qualité des résultats produits.----------ABSTRACT: The automated video surveillance systems currently deployed around the world are still quite far in terms of capabilities from the ones that have inspired countless science fiction works over the past few years. One of the reasons behind this lag in development is the lack of lowlevel tools that allow raw image data to be processed directly in the field. This preprocessing is used to reduce the amount of information transferred to centralized servers that have to interpret the captured visual content for further use. The identification of objects of interest
in raw images based on motion is an example of a reprocessing step that might be required by a large system. However, in a surveillance context, the preprocessing method can seldom rely on an appearance or shape model to recognize these objects since their exact nature cannot be known exactly in advance. This complicates the elaboration of low-level image processing methods. In this thesis, we present different methods that detect and segment objects of interest from video sequences in a fully unsupervised fashion. We first explore monocular video segmentation
approaches based on background subtraction. These approaches are based on the idea that the background of an observed scene can be modeled over time, and that any drastic
variation in appearance that is not predicted by the model actually reveals the presence of an intruding object. The main challenge that must be met by background subtraction methods is that their model should be able to adapt to dynamic changes in scene conditions. The designed methods must also remain sensitive to the emergence of new objects of interest despite this increased robustness to predictable dynamic scene behaviors. We propose two methods that introduce different modeling techniques to improve background appearance description in an illumination-invariant way, and that analyze local background persistence to improve the detection of temporarily stationary objects. We also introduce new feedback mechanisms used to adjust the hyperparameters of our methods based on the observed dynamics of the scene and the quality of the generated output