Abstract: A skeleton detection system stores a target image for use in skeleton detection, and a plurality of skeleton detection models respectively corresponding to a plurality of skeleton definition models for defining different skeletons. The skeleton detection system determines a predetermined condition for the skeleton detection of the target image, selects a first skeleton detection model from the plurality of skeleton detection models based on a result of the determination, and executes the skeleton detection of the target image by the first skeleton detection model.

Abstract: An object of the present invention is to provide a video analysis system and a video analysis method in which ownership between a person and baggage can be comprehensively determined. In a system that analyzes videos photographed by plural cameras, a detection/tracking process is performed for first and second objects using videos of plural cameras, and a relationship degree between the first and second objects is determined on the basis of the types of the first and second objects and a distance between the objects to be stored in a database.

Abstract: A probabilistic inference system is provided with: a pre-modification model input unit that receives the input of a probabilistic inference model; a model modification execution unit that outputs a modified probabilistic inference model; an inference calculation cost estimation unit that calculates a calculation cost when a probabilistic inference process is performed; an inference error estimation unit that estimates the magnitude of inference error that could be caused in a certain designated random variable in the probabilistic inference model when the probabilistic inference process is performed using the modified probabilistic inference model, compared with when the probabilistic inference process is performed using the probabilistic inference model; an adopted model selection unit that selects the probabilistic inference model to be adopted based on a probabilistic inference condition regarding the calculation cost and the inference error; and a modified model output unit that outputs the adopted probab

Abstract: A chronological change prediction system stores a graphical model which an evidence set at a first time is inputted in and outputs information about states of random variables at a second time when a first period has passed from the first time, determines whether to convert each probability distribution in the first evidence set based on a feature value calculated from the probability distributions and/or a feature value calculated from a random variable associated with the probability distributions in the graphical model, converts each probability distribution determined to be converted into a particular state of a random variable corresponding to each probability distribution determined to be converted, creates a second evidence set from the first evidence set by replacing each probability distribution determined to be converted and included in the first evidence set with the first particular state, and inputs the second evidence set to the graphical model.

Abstract: A controller of a distributed computing system assigns feature vectors, and assigns data processors and a model updater to first computers. The data processors have charge of iteration calculation of machine learning algorithms, acquire the feature vectors over a network when starting learning, and store the feature vectors in a local storage. In iteration of second and subsequent learning processes, the data processors load the feature vectors from the local storage, and conduct the learning process. The feature vectors are retained in the local storage till completion of learning. The data processors send only the learning results to the model updater, and waits for a next input from the model updater. The model updater conducts the initialization, integration, and convergence check of the model parameters, completes the processing if the model parameters are converged, and transmits new model parameters to the data processor if the model parameters are not converged.