Nec Corporation

Junnan Li

Jianquan Liu

The present invention provides an image processing apparatus () including a detection unit () that detects a plurality of predetermined points of a body of each of a plurality of persons from an image in an image circle of a fisheye image, a gravity direction determination unit () that determines a gravity direction in a position of each of the plurality of persons from the plurality of predetermined points, a reference point decision unit () that decides a reference point, based on the gravity direction in the position of each of the plurality of persons, a complementary circular image generation unit () that generates a complementary circular image that is a circular image acquired by adding a complementary image to the image in the image circle of the fisheye image, and that has, as a center, the reference point different from a center of the image in the image circle, and an expansion unit () that panoramically expands the complementary circular image, based on the reference point, and generates a panoramic image.

Image processing device, image processing method, and non-transitory storage medium

The present invention provides a processing apparatus () including a first generation unit () that generates, from a plurality of time-series images, three-dimensional feature information indicating a time change of a feature in each position in each of the plurality of images, a second generation unit () that generates person position information indicating a position in which a person is present in each of the plurality of images, and an estimation unit () that estimates person behavior indicated by the plurality of images, based on the time change of the feature indicated by the three-dimensional feature information in the position in which the person is present being indicated by the person position information.

Three-dimensional person behavior estimation

The present invention provides a processing system () including: a sample image generation unit () that generates a plurality of sample images being each associated with a partial region of a first image generated using a first lens; an estimation unit () that generates an image content estimation result indicating a content for each of the sample images using an estimation model generated by machine learning using a second image generated using a second lens differing from the first lens; a task execution unit () that estimates a relative positional relationship of a plurality of the sample images in the first image; a determination unit () that determines whether an estimation result of the relative positional relationship is correct; and a correction unit () that corrects a value of a parameter of the estimation model when the estimation result of the relative positional relationship is determined to be incorrect.

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Tokyo, Japan

Junnan Li