Object-Based Rendering and 3D reconstruction Using a Moveable Image-Based System

published_or_final_versio

Digital Video Stabilization

Ph.DDOCTOR OF PHILOSOPH

Estabilização digital de vídeos : algoritmos e avaliação

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O desenvolvimento de equipamentos multimídia permitiu um crescimento significativo na produção de vídeos por meio de câmeras, celulares e outros dispositivos móveis. No entanto, os vídeos capturados por esses dispositivos estão sujeitos a movimentos indesejados devido à vibração da câmera. Para superar esse problema, a estabilização digital visa remover o movimento indesejado dos vídeos pela aplicação de ferramentas computacionais, sem o uso de hardware específico, para melhorar a qualidade visual das cenas de forma a melhorar aspectos do vídeo segundo a percepção humana ou facilitar aplicações finais, como detecção e rastreamento de objetos. O processo de estabilização digital de vídeos bidimensional geralmente é dividido em três etapas principais: estimativa de movimento da câmera, remoção do movimento indesejado e geração do vídeo corrigido. Neste trabalho, investigamos e avaliamos métodos de estabilização digital de vídeos para corrigir vibrações e instabilidades que ocorrem durante o processo de aquisição. Na etapa de estimativa de movimento, desenvolvemos e analisamos um método consensual para combinar um conjunto de técnicas de características locais para estimativa do movimento global. Também apresentamos e testamos uma nova abordagem que identifica falhas na estimativa do movimento da câmera por meio de técnicas de otimização e calcula uma estimativa corrigida. Na etapa de remoção do movimento indesejável, propomos e avaliamos uma nova abordagem para estabilização de vídeos com base em um filtro Gaussiano adaptativo para suavizar a trajetória da câmera. Devido a incoerências existentes nas medidas de avaliação disponíveis na literatura em relação à percepção humana, duas representações são propostas para avaliar qualitativamente os métodos de estabilização de vídeos: a primeira baseia-se em ritmos visuais e representa o comportamento do movimento do vídeo, enquanto que a segunda é baseada na imagem da energia do movimento e representa a quantidade de movimento presente no vídeo. Experimentos foram realizados em três bases de dados. A primeira consiste em onze vídeos disponíveis na base de dados GaTech VideoStab e outros três vídeos coletados separadamente. A segunda, proposta por Liu et al., consiste em 139 vídeos divididos em diferentes categorias. Finalmente, propomos uma base de dados complementar às demais, composta a partir de quatro vídeos coletados separadamente. Trechos dos vídeos originais com presença de objetos em movimento e com fundo pouco representativo foram extraídos, gerando-se um total de oito vídeos. Resultados experimentais demonstraram a eficácia das representações visuais como medida qualitativa para avaliar a estabilidade dos vídeos, bem como o método de combinação de características locais. O método proposto baseado em otimização foi capaz de detectar e corrigir falhas de estimativa de movimento, obtendo resultados significativamente superiores em relação à não aplicação dessa correção. O filtro Gaussiano adaptativo permitiu gerar vídeos com equilíbrio adequado entre a taxa de estabilização e a quantidade de pixels preservados nos quadros dos vídeos. Os resultados alcançados como o nosso método de otimização nos vídeos da base de dados proposta foram superiores aos obtidos pelo método implementado no YouTubeAbstract: The development of multimedia equipments has allowed a significant growth in the production of videos through professional and amateur cameras, smartphones and other mobile devices. However, videos captured by these devices are subject to unwanted vibrations due to camera shaking. To overcome such problem, digital stabilization aims to remove undesired motion from videos through software techniques, without the use of specific hardware, to enhance visual quality either with the intention of enhancing human perception or improving final applications, such as detection and tracking of objects. The two-dimensional digital video stabilization process is usually divided into three main steps: camera motion estimation, removal of unwanted motion, and generation of the corrected video. In this work, we investigate and evaluate digital video stabilization methods for correcting disturbances and instabilities that occur during the process of video acquisition. In the motion estimation step, we develop and analyzed a consensual method for combining a set of local feature techniques for global motion estimation. We also introduce and test a novel approach that identifies failures in the global motion estimation of the camera through optimization and computes a new estimate of the corrected motion. In the removal of unwanted motion step, we propose and evaluate a novel approach to video stabilization based on an adaptive Gaussian filter to smooth the camera path. Due to the incoherence of assessment measures available in the literature regarding human perception, two novel representations are proposed for qualitative evaluation of video stabilization methods: the first is based on the visual rhythms and represents the behavior of the video motion, whereas the second is based on the motion energy image and represents the amount of motion present in the video. Experiments are conducted on three video databases. The first consists of eleven videos available from the GaTech VideoStab database, and three other videos collected separately. The second, proposed by Liu et al., consists of 139 videos divided into different categories. Finally, we propose a database that is complementary to the others, composed from four videos collected separately, which are excerpts from the original videos with moving objects in the foreground and with little representative background extracted, resulting in eight final videos. Experimental results demonstrated the effectiveness of the visual representations as qualitative measure for evaluating video stability, as well as the combination method over individual local feature approaches. The proposed method based on optimization was able to detect and correct the motion estimation failures, achieving considerably superior results compared to when this correction is not applied. The adaptive Gaussian filter allowed to generate videos with adequate trade-off between stabilization rate and amount of frame pixels. The results reached with our optimization method for the videos of the proposed database were superior to those obtained with YouTube's state-of-the-art methodMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

Real-time video stabilization without phantom movements for micro aerial vehicles

In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.Peer ReviewedPostprint (published version

StableFlow: a physics inspired digital video stabilization

This thesis addresses the problem of digital video stabilization. With the widespread use of handheld devices and unmanned aerial vehicles (UAVs) that has the ability to record videos, digital video stabilization becomes more important as the videos are often shaky undermining the visual quality of the video. Digital video stabilization has been studied for decades yielding an extensive amount of literature in the field, however, current approaches suffer from either being computationally expensive or under-performing in terms of visual quality . In this thesis, we firstly introduce a novel study of the effect of image denoising on feature-based digital video stabilization. Then, we introduce SteadyFlow, a novel technique for real-time stabilization inspired by the mass spring damper model. A video frame is modelled as a mass suspended in each direction by a critically dampened spring and damper which can be fine-tuned to adapt with different shaking patterns. The proposed technique is tested on video sequences that have different types of shakiness and diverse video contents. The obtained results significantly outperforms state-of-the art stabilization techniques in terms of visual quality while performing in real time

Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles

A Video Stabilization Method Based on Inter-frame Image Matching Scorea

Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. In this paper, we propose an robust and efficient video stabilization algorithm based on inter-frame image matching score. Firstly, image matching is performed by a method combining Maximally Stable Extremal Regions (MSERs) detection algorithm and Features from Accelerated Segment Test (FAST) corner detection algorithm, which can get the matching score and the motion parameters of the frame image. Then, the matching score is filtered to filter out the high frequency component and keep the low frequency component. And the motion compensation is performed on the current frame image according to the ratio of the matching score before and after the filtering to retain the global motion and remove the local jitter. Various classical corner detection operators and region matching operators are compared in experiments. And experimental results illustrate that the proposed method is effective to stabilize translational, rotational, and zooming jitter and robust to local motions, and has the state-of-the-art processing speed to meet the needs of real-time equipment