A genetic algorithm for optical flow estimation

Tagliasacchi, Marco

doi:10.1016/j.imavis.2006.01.021

Cited by 18 publications

(8 citation statements)

References 12 publications

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“…Optical flow is the velocity of model motion in an image, and optical flow field is a 2D instantaneous velocity field, and it is the projection of 3D velocity field in imaging plain [6,7]. The optical method is based on a brightness constancy model, that's to say that it assumes that the brightness of a pixel in an image is unchangeable.…”

Section: A Lucas-kanade Optical Flow Methodsmentioning

confidence: 99%

Indirect human activity recognition based on optical flow method

Yin

Wei

et al. 2012

2012 5th International Congress on Image and Signal Processing

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A new method to recognize human activity with videos from a wearable camera is proposed in this paper. With a camera mounted to a human body, the moving subject won't appear in the video when the person is in some motions. But we can estimate the activity from the changes of scenes in videos. Optical flow method is a common method to calculate motion vectors of objects in two adjacent images. For higher precision, in this paper, we use Lucas-Kanade optical flow method with pyramid structure to calculate the optical flow of scenes which can reflect people's motion to some extents. When key information is extracted from the optical flow field, we design a feature descriptor to describe the motion in frames in a video. The feature descriptor contains angels, bounce information and other important information which can distinguish different motion. After getting feature descriptors, we use support vector machine to classify different motions with a machine learning method. Experimental results show that our method successfully identifies motion such as walking, running, going upstairs and going downstairs.Compared with methods based on blocking-matching, this method has fewer costs and has higher precision.

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Section: A Lucas-kanade Optical Flow Methodsmentioning

confidence: 99%

Indirect human activity recognition based on optical flow method

Yin

Wei

et al. 2012

2012 5th International Congress on Image and Signal Processing

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“…Usually, they work as extraction of a parametric motion for each layer. Genetic algorithms also can be used for computation of flow (Tagliasacchi [32]).…”

Section: Prior Approachesmentioning

confidence: 99%

Periodicity-Based Computation of Optical Flow

Khachaturov

Brambila

Trejo

2015

CyS

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The standard Brightness Constancy Equation states spatiotemporal shift invariance of the input data along a local velocity of optical flow. In its turn, the shift invariance leads to a periodic function of a real argument. This allows application of a known test for periodicity to computation of optical flow at random locations. The approach is valid also for higher dimensions: for example, it applies to a sequence of 3D tomography images. The proposed method has a reasonably high accuracy for continuous flow and is noise tolerant. Special attention is paid to weak signal input. It is shown that a drastic reduction in the signal strength worsens the accuracy of estimates insignificantly. For a possible application to tomography, this would lead to an unprecedented diminution of harmful radiation exposure.

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“…Elles s'appuient sur l'hypothèse de conservation de la luminance au cours du temps considérée comme valide pour des mouvements de faible amplitude entre deux images successives [16][17][18][19][20]. La prise en compte de variations des conditions d'illumination, de réflexions spéculaires ou d'occlusions a cependant amené de nouvelles formulations plus générales en particulier celle de Papenberg et al [21] : …”

Section: Les Méthodes Différentiellesunclassified

Analyse de mouvement : une revue

Laguitton

Toumoulin

2009

IRBM

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Reçu le 21 décembre 2008 ; accepté le 22 décembre 2008 Disponible sur Internet le 5 février 2009Résumé L'analyse des séquences temporelles d'images a fait l'objet de nombreux travaux depuis plus de 20 ans. Elle permet d'accéder à des informations importantes sur les objets contenus dans les scènes observées que ce soit à des fins de détection, d'estimation, de suivi ou de reconnaissance. Elle conduit aussi à de nombreuses applications dans des domaines très divers allant de la télévision à la météorologie, de la robotique à la surveillance ou encore au médical. Cette importance reconnue et la diversité des problématiques qu'elle recouvre ont motivé cet état de l'art qui se positionne dans un cadre générique plutôt qu'applicatif de façon à donner aux lecteurs une vue d'ensemble. Après avoir introduit les éléments clés à considérer (objets, observations, objectifs), trois grandes familles de méthodes sont analysées et discutées : les approches différentielles, les techniques bayesiennes et les méthodes de mise en correspondance. Un dernier paragraphe s'attache à faire le lien avec l'imagerie médicale en soulignant les spécificités qu'elle recouvre. AbstractImage sequence analysis has been a major topic for years. It allows accessing important information on the objects of interest for detection, estimation, tracking and recognition tasks. Many applications are concerned from video to weather forecasting, robotics to in-door monitoring up to medicine. The place that it has today and the diversity of the problems to address have motivated this state-of-the-art. A generic approach has been adopted in order to provide a full view on the recent advances reported in the literature at the methodological level. After introducing the key elements to be considered (objects, observations, objectives), three main classes of methods are analysed and discussed: the differential approaches, the Bayesian techniques and the matching methods. A last section is focused on the links with medical imaging with the aim to emphasize the special features to deal with.

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A genetic algorithm for optical flow estimation

Cited by 18 publications

References 12 publications

Indirect human activity recognition based on optical flow method

Indirect human activity recognition based on optical flow method

Periodicity-Based Computation of Optical Flow

Analyse de mouvement : une revue

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