Имя автора: image

Detection and classification of dynamic objects using 3D virtual models

Tracking moving objects in video using stationary or mobile video cameras Stationary camera (with fixed position).
Purpose: automatic detection and real-time tracking of objects in the monitored area.
General method: common use of statistical modeling and background subtraction on video images with kernelized correlation filter (KCF). https://image.irtc.org.ua/wp-content/uploads/2025/07/Zaliznichnik.mp4 Mobile camera.General method: tracking of objects in video based on the complementary use of the KCF algorithm with HOG features and correlation filter with HSV histogram of object color features. Examples of detection and tracking: https://image.irtc.org.ua/wp-content/uploads/2025/07/Treking2.mp4 RELATED PAPERS: Відстеження в реальному часі об’єктів у відео на основі адаптивних гістограмних ознак. Простеження об’єктів під час відеоспостереження 

Stabilization of videos obtained from UAVs https://image.irtc.org.ua/wp-content/uploads/2025/07/Stabilizaciya.mp4 An algorithm has been developed that eliminates defects in video images that are formed when shooting with a camera with a sequential shutter. Such distortions cannot be corrected only by affine transformations, so the new algorithm deforms the image by shifting the lines vertically and horizontally. The algorithm is implemented as a program in the C++ language and its operation has been tested on videos from unmanned aerial vehicles, where a significant improvement in image quality has been demonstrated.   Розроблено алгоритм, що усуває дефекти на відео зображенні, які утворюються зйомкою камерою з послідовним затвором. Такі спотворення не можуть бути виправлені лише афінними перетвореннями, тому новий алгоритм деформує зображення зсувом рядків по вертикалі та горизонталі. Алгоритм реалізовано у вигляді програми на мові C++ та перевірено його роботу на відео з безпілотних літальних апаратів, де продемонстровано суттєве покращення якості зображення. RELATED PAPERS: Цифрова стабілізація відео: усунення дефектів послідовного затвора

Face 3D​   WHAT IS FACE 3D? From user’s point of view, Face 3D is simply a box, into which you may pass an image of one’s face, or a few of them, and get a 3D model of the face. HOW IT WORKS?   Face 3D has a set of vectors, representing different facial shapes, iånside. We call this set a Morphable model. Points in these vectors are arranged in such a way that semantically identical points of different faces have the same coordinate indices. For example, if tip of the nose in model s1 is on the first position then tips of the nose in all the other models s2, …,sk are also on the first positions. Shapes of all other faces are assumed to be linear combinations of those, comprised in our Morphable model. Thus, given a set of input images, Face 3D determines a linear combination which best matches to them. In order to do so, it also has to recover rotation and illumination of the face for each input image.   BUILDING A MORPHABLE MODEL   Although, facial shape recovery on the base of a Morphable model requires much effort, a Morphable model construction is a tough problem itself, since one has to establish correspondence between semantically identical points of the models. Moreover, input models contain different number of points making the task even more difficult. Furthermore, input models are not textured, so point correspondence has to be recovered taking into account facial shape only. We’ve managed to overcome all the mentioned challenges and built such a Morphable model by reducing the problem to motion field recovery task applying some reasonable point-wise similarity function. This task is formulated as a MaxSum labeling problem. SEE IT IN ACTION https://image.irtc.org.ua/wp-content/uploads/2025/07/Demo.mov   RELATED PAPERS:  3D Reconstruction of Human Face Based on Single or Several Images  (in English)  Відновлення просторової конфігурації людського обличчя за його фотознімком на основі генеративної моделі  (in Ukrainian)