Hitachi, Ltd.

Mohit Chhabra

Tomokazu Murakami

Martin Klinkigt

Junichi Kimura

Atsushi Hiroike

An object of the disclosure is to provide flexible and highly accurate activity detection means. Provided is an activity detection device including: an input unit that inputs an image sequence including a first image and a second image; an object detection unit that detects a first object in the image sequence; a component model unit that generates first characteristic information characterizing the first object and includes at least one individually trainable component model; and an activity detection unit that generates a first object state corresponding to the first object in the first image and a second object state corresponding to the first object in the second image based on the first characteristic information and determines an activity related to the first object based on the first and second object states.

Activity detection device, activity detection system, and activity detection method

In a case of receiving an access request to a target image, an image access management device can provide an appropriate access right holder with an appropriate range of information by determining a browsing level with respect to the target image according to an access authority or purpose included in the access request to the target image, by an access management unit, by generating a final image that corresponds to the access authority by processing the feature vector according to the browsing level, and providing the generated final image as a response to the access request, by an image generation unit.

Image access management device, image access management method, and image access management system

Aspects of the disclosure relate to a method for discovering latent factors from data in a neural network environment. Aspects include adding, by a data noise unit of the neural network environment, noise to a set of input data; computing, by the encoder model and a set of stochastic non-linear units, a set of latent code based on the set of input data; obtaining, by decoding the latent code with a decoder model, a set of reconstructed data.

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Tokyo, Japan

Mohit Chhabra