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Discriminative tracking has become popular tracking methods due to their descriptive power for foreground/background separation. Among these methods, online random forest is recently proposed and received a large amount of research attention due to its advantages such as efficiency and robustness to noise, etc. However, the fact that only one kind of features is used limits the discriminative performance...
This paper provides a novel method for visual object tracking based on the combination of local scale-invariant feature transform (SIFT) description and global incremental principal component analysis (PCA) representation in loosely constrained conditions. The state of object is defined by the position and shape of a parallelogram, which means that tracking results are given by locating the object...
In the field of nanotechnology, tracking freely swimming microorganisms under a microscope is difficult. An image of the target often rotates and changes shape as it moves. Further, the difficulty increases with background changes and fluctuations of colors and the brightness because of differences in the surrounding environment. To address these problems, we propose a tracking method combined with...
This paper presents a novel non-rigid object localization and segmentation algorithm using an eigenspace representation. Previous approaches to eigenspace methods for object tracking use vectorized image regions as observations, whereas the proposed method uses each individual pixel as an observation. Localization using the pixel-wise eigenspace representation is robust to noise and occlusions. A...
We propose a tracking system that is especially well-suited to tracking targets which change drastically in size or appearance. To accomplish this, we employ a fast, two phase template matching algorithm along with a periodic template update method. The template matching step ensures accurate localization while the template update scheme allows the target model to change over time along with the appearance...
The paper describes the techniques for the autonomous detection of moving targets by processing a sequence of sensor imageries in remote sensing applications. Two detection algorithms, which do not need a matrix inversion, are developed by extension of Hotellingpsilas principal-component method showing excellent performance and robustness. The detection of small, barely discernible, moving objects...
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