Abstract: An object recognition apparatus and method thereof are disclosed. An exemplary apparatus may determine an image feature vector of a first image by applying a convolution network to the first image. The convolution network may extract features from image learning sets that include the first image and a sample segmentation map of the first image. The exemplary apparatus may determine a segmentation map of the first image by applying a deconvolution network to the determined image feature vector. The exemplary apparatus may determine a weight of the convolution network and a weight of the deconvolution network based on the sample segmentation map and the first segmentation map. The exemplary apparatus may determine a second segmentation map of a second image through the convolution network using the determined weight of the convolution network and through the deconvolution network using the determined weight of the deconvolution network.

Abstract: A method by which a tracking apparatus tracks a target object includes: acquiring a first tree structure indicating a tracking processing order of frames, each frame including a tracking area in which the target object is located; acquiring a plurality of frame groups, each frame group consisting of two frames, and acquiring distance evaluation values of the respective frame groups; acquiring a second tree structure based on the first tree structure and the distance evaluation values; and tracking the target object based on the acquired second tree structure, wherein the distance evaluation value is determined based on at least one of locations of tracking areas included in two frames belonging to the frame group and pixel values included in the tracking areas.

Abstract: A system and method for modeling a dynamic system using Bayesian filtering, includes a prediction module to predict a state model of the dynamic system, the prediction module generates a prediction density having at least one mode, the state model includes a conditional density function including at least one kernel. Approximating module approximates a measurement probability from a sample set through at least one kernel and an update module updates the conditional density function using the measurement probability and the prediction density. A mode finding and mixture reduction module reduces the number of kernels in the conditional density function.

Abstract: A system and method for sequential kernel density approximation uses mode propagation to determine mode locations by the mean-shift method and reduce the generated modes. Hessians are calculated corresponding to the mode locations to determine the covariance. Density is approximated to update the density function depending on whether the Hessian is a negative indefinite.

Abstract: A system and method for sequential kernel density approximation uses mode propagation to determine mode locations by the mean-shift method and reduce the generated modes. Hessians are calculated corresponding to the mode locations to determine the covariance. Density is approximated to update the density function depending on whether the Hessian is a negative indefinite.

Abstract: A system and method for modeling a dynamic system using Bayesian filtering, includes a prediction module to predict a state model of the dynamic system, the prediction module generates a prediction density having at least one mode, the state model includes a conditional density function including at least one kernel. Approximating module approximates a measurement probability from a sample set through at least one kernel and an update module updates the conditional density function using the measurement probability and the prediction density. A mode finding and mixture reduction module reduces the number of kernels in the conditional density function.