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

Articulated human tracking and behavioural analysis in video sequences

Recently, there has been a dramatic growth of interest in the observation and tracking of human subjects through video sequences. Arguably, the principal impetus has come from the perceived demand for technological surveillance, however applications in entertainment, intelligent domiciles and medicine are also increasing. This thesis examines human articulated tracking and the classi cation of human movement, rst separately and then as a sequential process. First, this thesis considers the development and training of a 3D model of human body structure and dynamics. To process video sequences, an observation model is also designed with a multi-component likelihood based on edge, silhouette and colour. This is de ned on the articulated limbs, and visible from a single or multiple cameras, each of which may be calibrated from that sequence. Second, for behavioural analysis, we develop a methodology in which actions and activities are described by semantic labels generated from a Movement Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was developed for human tracking that allows multi-level parameter search consistent with the body structure. This tracker relies on the articulated motion prediction provided by the MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to generate a probabilistic activity description with action labels. The implemented algorithms for tracking and behavioural analysis are tested extensively and independently against ground truth on human tracking and surveillance datasets. Dynamic models are shown to predict and generate synthetic motion, while MCM recovers both periodic and non-periodic activities, de ned either on the whole body or at the limb level. Tracking results are comparable with the state of the art, however the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS

Algorithms for the reconstruction, analysis, repairing and enhancement of 3D urban models from multiple data sources

Over the last few years, there has been a notorious growth in the field of digitization of 3D buildings and urban environments. The substantial improvement of both scanning hardware and reconstruction algorithms has led to the development of representations of buildings and cities that can be remotely transmitted and inspected in real-time. Among the applications that implement these technologies are several GPS navigators and virtual globes such as Google Earth or the tools provided by the Institut Cartogràfic i Geològic de Catalunya. In particular, in this thesis, we conceptualize cities as a collection of individual buildings. Hence, we focus on the individual processing of one structure at a time, rather than on the larger-scale processing of urban environments. Nowadays, there is a wide diversity of digitization technologies, and the choice of the appropriate one is key for each particular application. Roughly, these techniques can be grouped around three main families: - Time-of-flight (terrestrial and aerial LiDAR). - Photogrammetry (street-level, satellite, and aerial imagery). - Human-edited vector data (cadastre and other map sources). Each of these has its advantages in terms of covered area, data quality, economic cost, and processing effort. Plane and car-mounted LiDAR devices are optimal for sweeping huge areas, but acquiring and calibrating such devices is not a trivial task. Moreover, the capturing process is done by scan lines, which need to be registered using GPS and inertial data. As an alternative, terrestrial LiDAR devices are more accessible but cover smaller areas, and their sampling strategy usually produces massive point clouds with over-represented plain regions. A more inexpensive option is street-level imagery. A dense set of images captured with a commodity camera can be fed to state-of-the-art multi-view stereo algorithms to produce realistic-enough reconstructions. One other advantage of this approach is capturing high-quality color data, whereas the geometric information is usually lacking. In this thesis, we analyze in-depth some of the shortcomings of these data-acquisition methods and propose new ways to overcome them. Mainly, we focus on the technologies that allow high-quality digitization of individual buildings. These are terrestrial LiDAR for geometric information and street-level imagery for color information. Our main goal is the processing and completion of detailed 3D urban representations. For this, we will work with multiple data sources and combine them when possible to produce models that can be inspected in real-time. Our research has focused on the following contributions: - Effective and feature-preserving simplification of massive point clouds. - Developing normal estimation algorithms explicitly designed for LiDAR data. - Low-stretch panoramic representation for point clouds. - Semantic analysis of street-level imagery for improved multi-view stereo reconstruction. - Color improvement through heuristic techniques and the registration of LiDAR and imagery data. - Efficient and faithful visualization of massive point clouds using image-based techniques.Durant els darrers anys, hi ha hagut un creixement notori en el camp de la digitalització d'edificis en 3D i entorns urbans. La millora substancial tant del maquinari d'escaneig com dels algorismes de reconstrucció ha portat al desenvolupament de representacions d'edificis i ciutats que es poden transmetre i inspeccionar remotament en temps real. Entre les aplicacions que implementen aquestes tecnologies hi ha diversos navegadors GPS i globus virtuals com Google Earth o les eines proporcionades per l'Institut Cartogràfic i Geològic de Catalunya. En particular, en aquesta tesi, conceptualitzem les ciutats com una col·lecció d'edificis individuals. Per tant, ens centrem en el processament individual d'una estructura a la vegada, en lloc del processament a gran escala d'entorns urbans. Avui en dia, hi ha una àmplia diversitat de tecnologies de digitalització i la selecció de l'adequada és clau per a cada aplicació particular. Aproximadament, aquestes tècniques es poden agrupar en tres famílies principals: - Temps de vol (LiDAR terrestre i aeri). - Fotogrametria (imatges a escala de carrer, de satèl·lit i aèries). - Dades vectorials editades per humans (cadastre i altres fonts de mapes). Cadascun d'ells presenta els seus avantatges en termes d'àrea coberta, qualitat de les dades, cost econòmic i esforç de processament. Els dispositius LiDAR muntats en avió i en cotxe són òptims per escombrar àrees enormes, però adquirir i calibrar aquests dispositius no és una tasca trivial. A més, el procés de captura es realitza mitjançant línies d'escaneig, que cal registrar mitjançant GPS i dades inercials. Com a alternativa, els dispositius terrestres de LiDAR són més accessibles, però cobreixen àrees més petites, i la seva estratègia de mostreig sol produir núvols de punts massius amb regions planes sobrerepresentades. Una opció més barata són les imatges a escala de carrer. Es pot fer servir un conjunt dens d'imatges capturades amb una càmera de qualitat mitjana per obtenir reconstruccions prou realistes mitjançant algorismes estèreo d'última generació per produir. Un altre avantatge d'aquest mètode és la captura de dades de color d'alta qualitat. Tanmateix, la informació geomètrica resultant sol ser de baixa qualitat. En aquesta tesi, analitzem en profunditat algunes de les mancances d'aquests mètodes d'adquisició de dades i proposem noves maneres de superar-les. Principalment, ens centrem en les tecnologies que permeten una digitalització d'alta qualitat d'edificis individuals. Es tracta de LiDAR terrestre per obtenir informació geomètrica i imatges a escala de carrer per obtenir informació sobre colors. El nostre objectiu principal és el processament i la millora de representacions urbanes 3D amb molt detall. Per a això, treballarem amb diverses fonts de dades i les combinarem quan sigui possible per produir models que es puguin inspeccionar en temps real. La nostra investigació s'ha centrat en les següents contribucions: - Simplificació eficaç de núvols de punts massius, preservant detalls d'alta resolució. - Desenvolupament d'algoritmes d'estimació normal dissenyats explícitament per a dades LiDAR. - Representació panoràmica de baixa distorsió per a núvols de punts. - Anàlisi semàntica d'imatges a escala de carrer per millorar la reconstrucció estèreo de façanes. - Millora del color mitjançant tècniques heurístiques i el registre de dades LiDAR i imatge. - Visualització eficient i fidel de núvols de punts massius mitjançant tècniques basades en imatges