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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Objektų judėjimo krypties ir skaičiaus nustatymas vaizdo kadruose / Determination of Number of Moving Objects and Their Movement Direction in Video

Riadovikovas, Sergejus 29 September 2008 (has links)
Daugumoje kompiuterinės regos sistemų yra svarbu identifikuoti kadro dalis kaip foną ar objektą. Jei yra įmanoma atskirti objektą nuo fono, tokios operacijos kaip atpažinimas, identifikavimas ir sekimas, gali būti taikomos šiam objektui. Fono atėmimas yra gana populiarus metodas išskirti pirmame plane esančius objektus ir foną. Šiuo metodu esamas kadras palyginamas su atraminiu fono kadru, po ko priimamas sprendimas, kuris kadro taškas priklauso fonui, o kuris dominančiam objektui. Šiame darbe siūlomas prisitaikantis fono modelio apskaičiavimo metodas. Metodo esmė yra tame, kad kiekvieno taško reikšmės pasirodymo laikas yra išsaugomas atmintyje ir naudojamas fono vaizdo, naudojamo fono ir kadro skirtumo operacijoje, atnaujinimui. Tikimasi, kad šis metodas gerai veiks vaizdo sekose, kuriose pavaizduoti miesto peizažo elemetai. Šis metodas tikriausiai neduos gerų rezultatų kadruose, kur objektai yra tos pačios spalvos, kaip ir juos supantis fonas. / In many computer vision systems it is important to classify parts of an image sequence as foreground or background. If it is possible to detect a foreground object further operations, such as recognition, identification or tracking, can be done on that object. Background subtraction is a particularly popular method to segment foreground and background. With this method the current image is compared with reference image of the background, and then the decision is made what is background and what is not by looking for changes at each pixel. In this thesis the adaptive background model calculation method is proposed. The key of the method is that the time of appearance of each pixel’s value is stored in memory and recalled later to update the background image used in subtraction operation to compute foreground objects. It is expected that this method will work well in ordinary image sequences where the foreground objects are the elements of urban scenery. The method probably will not work as well for objects which are of one color as the background because these pixels will be marked as background.

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