A Deep Moving-camera Background Model

In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-solved task, in the case of a Moving-camera Background Model (MCBM), the success has been far more modest due to algorithmic and scalability challenges that arise due to the camera motion. Thus, existing MCBMs are limited in their scope and their supported camera-motion types. These hurdles also impeded the employment, in this unsupervised task, of end-to-end solutions based on deep learning (DL). Moreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion. Unfortunately, the former creates several problems, including poor scalability, while the latter prevents the recognition and leveraging of cases where the camera revisits previously-seen parts of the scene. This paper proposes a new method, called DeepMCBM, that eliminates all the aforementioned issues and achieves state-of-the-art results. Concretely, first we identify the difficulties associated with joint alignment of video frames in general and in a DL setting in particular. Next, we propose a new strategy for joint alignment that lets us use a spatial transformer net with neither a regularization nor any form of specialized (and non-differentiable) initialization. Coupled with an autoencoder conditioned on unwarped robust central moments (obtained from the joint alignment), this yields an end-to-end regularization-free MCBM that supports a broad range of camera motions and scales gracefully. We demonstrate DeepMCBM's utility on a variety of videos, including ones beyond the scope of other methods. Our code is available at https://github.com/BGU-CS-VIL/DeepMCBM .Comment: 26 paged, 5 figures. To be published in ECCV 202

Robust Algorithms for Low-Rank and Sparse Matrix Models

Data in statistical signal processing problems is often inherently matrix-valued, and a natural first step in working with such data is to impose a model with structure that captures the distinctive features of the underlying data. Under the right model, one can design algorithms that can reliably tease weak signals out of highly corrupted data. In this thesis, we study two important classes of matrix structure: low-rankness and sparsity. In particular, we focus on robust principal component analysis (PCA) models that decompose data into the sum of low-rank and sparse (in an appropriate sense) components. Robust PCA models are popular because they are useful models for data in practice and because efficient algorithms exist for solving them. This thesis focuses on developing new robust PCA algorithms that advance the state-of-the-art in several key respects. First, we develop a theoretical understanding of the effect of outliers on PCA and the extent to which one can reliably reject outliers from corrupted data using thresholding schemes. We apply these insights and other recent results from low-rank matrix estimation to design robust PCA algorithms with improved low-rank models that are well-suited for processing highly corrupted data. On the sparse modeling front, we use sparse signal models like spatial continuity and dictionary learning to develop new methods with important adaptive representational capabilities. We also propose efficient algorithms for implementing our methods, including an extension of our dictionary learning algorithms to the online or sequential data setting. The underlying theme of our work is to combine ideas from low-rank and sparse modeling in novel ways to design robust algorithms that produce accurate reconstructions from highly undersampled or corrupted data. We consider a variety of application domains for our methods, including foreground-background separation, photometric stereo, and inverse problems such as video inpainting and dynamic magnetic resonance imaging.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143925/1/brimoor_1.pd

Aerial detection of ground moving objects

Automatic detection of ground moving objects (GMOs) from aerial camera platforms (ACPs) is essential in many video processing applications, both civilian and military. However, the extremely small size of GMOs and the continuous shaky motion of ACPs present challenges in detecting GMOs for traditional methods. In particular, existing detection methods fail to balance high detection accuracy and real-time performance. This thesis investigates the problem of GMOs detection from ACPs and overcoming the challenges and drawbacks that exist in traditional detection methods. The underlying assumption used in this thesis is based on principal component pursuits (PCP) in which the background of an aerial video is modelled as a low-rank matrix and the moving objects are modelled as sparse corrupting this video. The research in this thesis investigates the proposed problem in three directions: (1) handling the shaky motion in ACPs robustly with minimal computational cost, (2) improving the detection accuracy and radically lowering false detections via penalization term, and (3) extending PCP’s formulation to achieve adequate real-time performance. In this thesis, a series of novel algorithms are proposed to show the evolution of our research towards the development of KR-LNSP, a novel robust detection method which is characterized by high detection accuracy, low computational cost, adaptability to shaky motion in ACPs, and adequate real-time performance. Each of the proposed algorithms is intensively evaluated using different challenging datasets and compared with current state-of-the-art methods

Object Detection in Data Acquired From Aerial Devices

The object detection task, both in images and in videos, has been the source of extraordinary advances with state-of-the-art architectures that can achieve close to perfect precision on large modern datasets. As a result, since these models are trained on large-scale datasets, most of them can adapt to almost any other real-world scenario if given enough data. Nevertheless, there is a specific scenario, aerial images, in which these models tend to perform worse due to their natural characteristics. The main problem differentiating typical object detection datasets from aerial object detection datasets is the object’s scale that needs to be located and identified. Moreover, factors such as the image’s brightness, object rotation and details, and background colours also play a crucial role in the model’s performance, no matter its architecture. Deep learning models make decisions based on the features they can extract from the training data. This technique works particularly well in standard scenarios, where images portray the object at a standard scale in which the object’s details are precise and allow the model to distinguish it from the other objects and background. However, when considering a scenario where the image is being captured from 50 meters above, the object’s details diminish considerably and, thus, logically, making it harder for deep learning models to extract meaningful features that will allow for the identification and localization of the said object. Nowadays, many surveillance systems use static cameras placed in pre-defined places; however, a more appropriate approach for some scenarios would be using drones to surveil a particular area with a specific route. More specifically, these types of surveillance would be adequate for scenarios where it is not feasible to cover the whole area with static cameras, such as wild forests. The first objective of this dissertation is to gather a dataset that focuses on detecting people and vehicles in wild-forest scenarios. The dataset was captured using a DJI drone in four distinct zones of Serra da Estrela. It contains instances captured under different weather conditions – sunny and foggy – and during different parts of the day – morning, afternoon and evening. In addition, it also includes four different types of terrain, earth, tar, forest, and gravel, and there are two classes of objects, person and vehicle. Later on, the second objective of this dissertation aims to precisely analyze how state-ofthe-art single-frame-based and video object detectors perform in the previously described dataset. The analysis focuses on the models’ performance related to each object class in every terrain. Given this, we can demonstrate the exact situations in which the different models stand out and which ones tend to perform the worse. Finally, we propose two methods based on the results obtained during the first phase of experiments, where each aims to solve a different problem that emerged from applying stateof-the-art models to aerial images. The first method aims to improve the performance of the video object detector models in certain situations by using background removal algorithms to delineate specific areas in which the detectors’ predictions are considered valid. One of the main problems with creating a high-quality dataset from scratch is the intensive and time-consuming annotation process after gathering the data. Regarding this, the second method we propose consists of a self-supervised architecture that aims to tackle the particular scarcity of high-quality aerial datasets. The main idea is to analyze the usefulness of unlabelled data in these problems and thus, avoid the immense time-consuming process of labelling the entirety of a full-scale aerial dataset. The reported results show that even with only a partially labelled dataset, it is possible to use the unlabelled data in a self-supervised matter to improve the model’s performance further.A tarefa de deteção de objetos, tanto em imagem como em vídeo, tem contribuído com inúmeros avanços extraordinários no que toca a arquiteturas inovadoras e ao desenvolvimento de conjuntos de dados cada vez mais completos e de qualidade. Nesse sentido, a maioria dos modelos consegue adaptar-se a quase qualquer cenário do mundo real – se existirem dados suficientes –, uma vez que estes modelos são treinados nestes grandes conjuntos de dados. No entanto, existe um cenário específico – as imagens aéreas –, e que devido às suas caraterísticas naturais, estes modelos tendem a mostrar um desempenho de menor qualidade. Contudo, a diferença de escala do próprio objeto que precisa de ser localizado e identificado é o principal aspeto que marca a diferença entre os conjuntos de imagens típicas e os conjuntos de imagens aéreas. Além disso, fatores como o brilho da imagem, a rotação do objeto, os detalhes do mesmo e as cores de fundo também desempenham um papel crucial no desempenho do modelo, independentemente da sua arquitetura. Modelos de aprendizagem profunda tomam decisões com base nas características que conseguem extrair do conjunto de imagens de treino. Esta técnica funciona particularmente bem em cenários padrão, em que as imagens representam o objeto numa escala normal, onde os detalhes do objeto são precisos e permitem que o modelo o distinga de outros objetos. Contudo, ao considerar um cenário onde a imagem está a ser capturada a 50 metros de altura, os detalhes do objeto diminuem consideravelmente e, portanto, torna-se mais difícil para o modelo extrair as melhores caraterísticas significativas que permitem a identificação e localização do objeto. Atualmente, muitos sistemas de vigilância utilizam câmaras estáticas colocadas em locais pré-definidos; porém, uma abordagem mais apropriada para alguns cenários poderia passar por utilizar drones de modo a vigiar uma determinada área com um percurso pré-definido. Mais especificamente, estes tipos de vigilância seriam adequados a cenários em que não é viável cobrir toda a área com câmaras, tal como florestas. O primeiro objetivo do presente trabalho passa por reunir um conjunto de dados que se foque na deteção de pessoas e veículos em florestas. O conjunto de dados foi capturado com um drone DJI em quatro zonas distintas da Serra da Estrela, e contém gravações que foram capturadas com diferentes condições meteorológicas – sol e nevoeiro – e durante diferentes fases do dia – manhã, tarde e ao anoitecer. Além do mais, contempla também quatro tipos diferentes de terreno, terra, alcatrão, floresta e gravilha, para além de existirem duas classes de objetos, pessoa e veículo. Posteriormente, o segundo objetivo contempla a análise precisa do modo como os detetores de objetos de vídeo e imagem atuam no conjunto de dados anteriormente descrito. A análise centra-se no desempenho dos modelos em relação a cada classe de objeto e a cada terreno. Com isto, conseguimos demonstrar uma perspetiva das situações exatas em que os diferentes tipos de modelos se destacam e quais os que tendem a não ter um desempenho tão adequado. Finalmente, com base nos resultados obtidos durante a primeira fase de experiências, o objetivo final tem como propósito propor dois métodos em que cada um deles visa resolver um problema diferente que surgiu da aplicação destes detetores em imagens aéreas. O primeiro método destaca a utilização de algoritmos de remoção de fundo para melhorar o desempenho dos modelos de deteção de objetos em vídeo em determinadas situações com o objetivo de delimitar áreas específicas nas quais as deteções dos modelos devem ser consideradas válidas. Um dos principais problemas na criação de um conjunto de dados de alta qualidade a partir do zero é o processo intensivo e moroso de anotação após a recolha dos dados. Com respeito a isto, o segundo método proposto consiste numa arquitetura auto-supervisionada que tem como objetivo enfrentar a escassez particular de conjuntos de dados aéreos de alta qualidade. A ideia principal é analisar a utilidade dos dados não anotados nestes projetos e, assim, evitar o processo demorado e custoso de anotar a totalidade de um conjunto de dados aéreos. Os resultados relatados mostram que, mesmo com um conjunto de dados parcialmente anotado, é possível utilizar os dados não anotados numa arquitetura auto-supervisionada para melhorar ainda mais o desempenho do modelo

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion