Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Exploitation de contraintes photométriques et géométriques en vision : application au suivi, au calibrage et à la reconstruction

Cette thése a été réalisée dans le cadre d'une cotutelle avec l'Institut National Polytechnique de Grenoble (France). La recherche a été effectuée au sein des laboratoires de vision 3D (DIRO, UdM) et PERCEPTION-INRIA (Grenoble).

Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

Non-rigid object tracking in complex scenes

A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

Deteção e acompanhamento de movimento através de uma câmara de vídeo

A deteção e seguimento de pessoas tem uma grande variedade de aplicações em visão computacional. Embora tenha sido alvo de anos de investigação, continua a ser um tópico em aberto, e ainda hoje, um grande desafio a obtenção de uma abordagem que inclua simultaneamente exibilidade e precisão. O trabalho apresentado nesta dissertação desenvolve um caso de estudo sobre deteção e seguimento automático de faces humanas, em ambiente de sala de reuniões, concretizado num sistema flexível de baixo custo. O sistema proposto é baseado no sistema operativo GNU's Not Unix (GNU) linux, e é dividido em quatro etapas, a aquisição de vídeo, a deteção da face, o tracking e reorientação da posição da câmara. A aquisição consiste na captura de frames de vídeo das três câmaras Internet Protocol (IP) Sony SNC-RZ25P, instaladas na sala, através de uma rede Local Area Network (LAN) também ele já existente. Esta etapa fornece os frames de vídeo para processamento à detecção e tracking. A deteção usa o algoritmo proposto por Viola e Jones, para a identificação de objetos, baseando-se nas suas principais características, que permite efetuar a deteção de qualquer tipo de objeto (neste caso faces humanas) de uma forma genérica e em tempo real. As saídas da deteção, quando é identificado com sucesso uma face, são as coordenadas do posicionamento da face, no frame de vídeo. As coordenadas da face detetada são usadas pelo algoritmo de tracking, para a partir desse ponto seguir a face pelos frames de vídeo subsequentes. A etapa de tracking implementa o algoritmo Continuously Adaptive Mean-SHIFT (Camshift) que baseia o seu funcionamento na pesquisa num mapa de densidade de probabilidade, do seu valor máximo, através de iterações sucessivas. O retorno do algoritmo são as coordenadas da posição e orientação da face. Estas coordenadas permitem orientar o posicionamento da câmara de forma que a face esteja sempre o mais próximo possível do centro do campo de visão da câmara. Os resultados obtidos mostraram que o sistema de tracking proposto é capaz de reconhecer e seguir faces em movimento em sequências de frames de vídeo, mostrando adequabilidade para aplicação de monotorização em tempo real.

Hand gesture recognition system based in computer vision and machine learning : applications on human-machine interaction

Sendo uma forma natural de interação homem-máquina, o reconhecimento de gestos implica uma forte componente de investigação em áreas como a visão por computador e a aprendizagem computacional. O reconhecimento gestual é uma área com aplicações muito diversas, fornecendo aos utilizadores uma forma mais natural e mais simples de comunicar com sistemas baseados em computador, sem a necessidade de utilização de dispositivos extras. Assim, o objectivo principal da investigação na área de reconhecimento de gestos aplicada à interacção homemmáquina é o da criação de sistemas, que possam identificar gestos específicos e usálos para transmitir informações ou para controlar dispositivos. Para isso as interfaces baseados em visão para o reconhecimento de gestos, necessitam de detectar a mão de forma rápida e robusta e de serem capazes de efetuar o reconhecimento de gestos em tempo real. Hoje em dia, os sistemas de reconhecimento de gestos baseados em visão são capazes de trabalhar com soluções específicas, construídos para resolver um determinado problema e configurados para trabalhar de uma forma particular. Este projeto de investigação estudou e implementou soluções, suficientemente genéricas, com o recurso a algoritmos de aprendizagem computacional, permitindo a sua aplicação num conjunto alargado de sistemas de interface homem-máquina, para reconhecimento de gestos em tempo real. A solução proposta, Gesture Learning Module Architecture (GeLMA), permite de forma simples definir um conjunto de comandos que pode ser baseado em gestos estáticos e dinâmicos e que pode ser facilmente integrado e configurado para ser utilizado numa série de aplicações. É um sistema de baixo custo e fácil de treinar e usar, e uma vez que é construído unicamente com bibliotecas de código. As experiências realizadas permitiram mostrar que o sistema atingiu uma precisão de 99,2% em termos de reconhecimento de gestos estáticos e uma precisão média de 93,7% em termos de reconhecimento de gestos dinâmicos. Para validar a solução proposta, foram implementados dois sistemas completos. O primeiro é um sistema em tempo real capaz de ajudar um árbitro a arbitrar um jogo de futebol robótico. A solução proposta combina um sistema de reconhecimento de gestos baseada em visão com a definição de uma linguagem formal, o CommLang Referee, à qual demos a designação de Referee Command Language Interface System (ReCLIS). O sistema identifica os comandos baseados num conjunto de gestos estáticos e dinâmicos executados pelo árbitro, sendo este posteriormente enviado para um interface de computador que transmite a respectiva informação para os robôs. O segundo é um sistema em tempo real capaz de interpretar um subconjunto da Linguagem Gestual Portuguesa. As experiências demonstraram que o sistema foi capaz de reconhecer as vogais em tempo real de forma fiável. Embora a solução implementada apenas tenha sido treinada para reconhecer as cinco vogais, o sistema é facilmente extensível para reconhecer o resto do alfabeto. As experiências também permitiram mostrar que a base dos sistemas de interação baseados em visão pode ser a mesma para todas as aplicações e, deste modo facilitar a sua implementação. A solução proposta tem ainda a vantagem de ser suficientemente genérica e uma base sólida para o desenvolvimento de sistemas baseados em reconhecimento gestual que podem ser facilmente integrados com qualquer aplicação de interface homem-máquina. A linguagem formal de definição da interface pode ser redefinida e o sistema pode ser facilmente configurado e treinado com um conjunto de gestos diferentes de forma a serem integrados na solução final.

Automated segmentation, tracking and evaluation of bacteria in microscopy images

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Hand tracking in high-speed camera videos

Visual object tracking has been one of the most popular research topics in the field of computer vision recently. Specifically, hand tracking has attracted significant attention since it would enable many useful practical applications. However, hand tracking is still a very challenging problem which cannot be considered solved. The fact that almost every aspect of hand appearance can change is the fundamental reason for this difficulty. This thesis focused on 2D-based hand tracking in high-speed camera videos. During the project, a toolbox for this purpose was collected which contains nine different tracking methods. In the experiments, these methods were tested and compared against each other with both high-speed videos recorded during the project and publicly available normal speed videos. The results revealed that tracking accuracies varied considerably depending on the video and the method. Therefore, no single method was clearly the best in all videos, but three methods, CT, HT, and TLD, performed better than the others overall. Moreover, the results provide insights about the suitability of each method to different types and situations of hand tracking.

Model-Based Tracking Initialization in Ship Building Environment

An augmented reality (AR) device must know observer’s location and orientation, i.e. observer’s pose, to be able to correctly register the virtual content to observer’s view. One possible way to determine and continuously follow-up the pose is model-based visual tracking. It supposes that a 3D model of the surroundings is known and that there is a video camera that is fixed to the device. The pose is tracked by comparing the video camera image to the model. Each new pose estimate is usually based on the previous estimate. However, the first estimate must be found out without a prior estimate, i.e. the tracking must be initialized, which in practice means that some model features must be identified from the image and matched to model features. This is known in literature as model-to-image registration problem or simultaneous pose and correspondence problem. This report reviews visual tracking initialization methods that are suitable for visual tracking in ship building environment when the ship CAD model is available. The environment is complex, which makes the initialization non-trivial. The report has been done as part of MARIN project.

Video-based postural sway analysis in a controlled environment

À mesure que la population des personnes agées dans les pays industrialisés augmente au fil de années, les ressources nécessaires au maintien du niveau de vie de ces personnes augmentent aussi. Des statistiques montrent que les chutes sont l’une des principales causes d’hospitalisation chez les personnes agées, et, de plus, il a été démontré que le risque de chute d’une personne agée a une correlation avec sa capacité de maintien de l’équilibre en étant debout. Il est donc d’intérêt de développer un système automatisé pour analyser l’équilibre chez une personne, comme moyen d’évaluation objective. Dans cette étude, nous avons proposé l’implémentation d’un tel système. En se basant sur une installation simple contenant une seule caméra sur un trépied, on a développé un algorithme utilisant une implémentation de la méthode de détection d’objet de Viola-Jones, ainsi qu’un appariement de gabarit, pour suivre autant le mouvement latéral que celui antérieur-postérieur d’un sujet. On a obtenu des bons résultats avec les deux types de suivi, cependant l’algorithme est sensible aux conditions d’éclairage, ainsi qu’à toute source de bruit présent dans les images. Il y aurait de l’intérêt, comme développement futur, d’intégrer les deux types de suivi, pour ainsi obtenir un seul ensemble de données facile à interpréter.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

Mean-tracking clustering algorithm for radial basis function centre selection

Radial basis functions can be combined into a network structure that has several advantages over conventional neural network solutions. However, to operate effectively the number and positions of the basis function centres must be carefully selected. Although no rigorous algorithm exists for this purpose, several heuristic methods have been suggested. In this paper a new method is proposed in which radial basis function centres are selected by the mean-tracking clustering algorithm. The mean-tracking algorithm is compared with k means clustering and it is shown that it achieves significantly better results in terms of radial basis function performance. As well as being computationally simpler, the mean-tracking algorithm in general selects better centre positions, thus providing the radial basis functions with better modelling accuracy

Automated identification and tracking of polar-cap plasma patches at solar minimum

A method of automatically identifying and tracking polar-cap plasma patches, utilising data inversion and feature-tracking methods, is presented. A well-established and widely used 4-D ionospheric imaging algorithm, the Multi-Instrument Data Assimilation System (MIDAS), inverts slant total electron content (TEC) data from ground-based Global Navigation Satellite System (GNSS) receivers to produce images of the free electron distribution in the polar-cap ionosphere. These are integrated to form vertical TEC maps. A flexible feature-tracking algorithm, TRACK, previously used extensively in meteorological storm-tracking studies is used to identify and track maxima in the resulting 2-D data fields. Various criteria are used to discriminate between genuine patches and "false-positive" maxima such as the continuously moving day-side maximum, which results from the Earth's rotation rather than plasma motion. Results for a 12-month period at solar minimum, when extensive validation data are available, are presented. The method identifies 71 separate structures consistent with patch motion during this time. The limitations of solar minimum and the consequent small number of patches make climatological inferences difficult, but the feasibility of the method for patches larger than approximately 500 km in scale is demonstrated and a larger study incorporating other parts of the solar cycle is warranted. Possible further optimisation of discrimination criteria, particularly regarding the definition of a patch in terms of its plasma concentration enhancement over the surrounding background, may improve results.