Nec Corporation

Nec C Orporation

Kyushu University

Hiroyoshi Miyano

Ryoma Oami

Yusuke Takahashi

Takuya Ogawa

Hiroo Ikeda

Hiroshi Yamada

Ryo Kawai

Kazuya Koyama

Yukie Ebiyama

Kenichiro Ida

Hiroshi Kitajima

Takashi Shibata

Tetsuaki Suzuki

Rui Ishiyama

Takafumi Koshinaka

Masato Toda

Tetsuo Inoshita

Masahiro Tani

Osamu Houshuyama

Toru Takahashi

Katsuhiko Takahashi

Kota Iwamoto

Emi Kitagawa

Yasuhiko Yoshida

Kyota Higa

Hideaki Sato

Eiji Kaneko

Azusa Sawada

Keiko Inoue

Tatsuya Abe

Yasunori Babazaki

Fumihiko Sakaue

Atsushi Shimada

Hidekata Hontani

Tsubasa Minematsu

Rin-Ichiro Taniguchi

An object tracking apparatus, method and computer-readable medium for detecting an object from output information of sensors, tracking the object on a basis of a plurality of detection results, generating tracking information of the object represented in a common coordinate system, outputting the tracking information, and detecting the object on a basis of the tracking information.

Object tracking apparatus, object tracking system, object tracking method, display control device, object detection device, and computer-readable medium

Provided is an image processing system, an image processing method, and a program for preferably detecting a mobile object. The image processing system includes: an image input unit for receiving an input for some image frames having different times in a plurality of image frames constituting a picture, which is of a pixel on which the mobile object appears or a pixel on which the mobile object does not appear, for selected arbitrary one or more pixels in an image frame at the time of processing; and a mobile object detection model constructing unit for learning a parameter for detecting the mobile object based on the input.

Image processing and object detecting system, image processing and object detecting method, and program storage medium

Vehicle detecting system, vehicle detecting method, and program storage medium

A guidance processing apparatus () includes an information acquisition unit () that acquires a plurality of different pieces of guidance information on the basis of states of a plurality of people within one or more images, and a control unit () that performs control of a plurality of target devices present in different spaces or time division control of a target device so as to set a plurality of different states corresponding to the plurality of pieces of guidance information.

Guidance processing apparatus and guidance method

[Problem] To provide a motion condition estimation device, a motion condition estimation method and a motion condition estimation program capable of accurately estimating the motion condition of monitored subjects even in a crowded environment. [Solution] A motion condition estimation device according to the present invention is provided with a quantity estimating means and a motion condition estimating means. The quantity estimating means uses a plurality of chronologically consecutive images to estimate a quantity of monitored subjects for each local region in each image. The motion condition estimating means estimates the motion condition of the monitored subjects from chronological changes in the quantities estimated in each local region.

Movement state estimation device, movement state estimation method and program recording medium

In a foreground extraction apparatus, an extraction result generation unit performs a foreground extraction using a plurality of foreground extraction models for an input image, and generates foreground extraction results. A selection unit selects one or more foreground extraction models among the plurality of foreground extraction models using respective foreground results acquired by the plurality of foreground extraction models. A foreground region generation unit extracts each foreground region based on the input image using the selected one or more foreground extraction models.

Foreground extraction apparatus, foreground extraction method, and recording medium

A learning apparatus () according to the present invention includes a detection unit () that detects, as a candidate region of a learning target, a region detected by one of first detection processing of detecting an object region from a predetermined image and second detection processing of detecting a change region from background image information and the image, and not detected by the other, an output unit () that outputs at least a part of the candidate region as a labeling target, and a learning unit () that learns a model for performing the first detection processing or a model for performing the second detection processing by using the labeled candidate region as learning data.

Learning apparatus, learning method, and non-transitory computer-readable medium in which learning program has been stored

A learning device makes an object detection device learn how to detect an object from an input image. A feature extraction unit performs feature extraction from input images including real images and pseudo images to generate feature maps, and the object detection unit detects objects included in the input images based on the feature maps. The domain identification unit identifies the domains forming the input images and generates domain identifiability information. Then, the feature extraction unit and the object detection unit learn common features that do not depend on the difference in domains, based on the domain identifiability information.

Learning device, object detection device, learning method, and recording medium

An information processing apparatus () generates likelihood data for each of a plurality of partial regions () in image data (). The likelihood data are data being associated with a position and a size on the image data () and indicating a likelihood that a target object exists in an image region at the position with the size. The information processing apparatus () computes a distribution (probability hypothesis density: PHD) of an existence likelihood of a target object with respect to a position and a size by computing the total sum of likelihood data each piece of which is generated for each partial region (). The information processing apparatus () extracts, from the PHD, partial distributions each of which relates to one target object. For each extracted partial distribution, the information processing apparatus () outputs a position and a size of a target object represented by the partial distribution, based on a statistic of the partial distribution.

Information processing device, control method, and program

A manufacturing management deviceincludes an acquisition unitthat acquires unique information of a product from a captured image of the product captured in a manufacturing step included in a manufacturing process of the product, and acquires operation information representing an operation of a person who performs a manufacturing work on the product in the manufacturing step, and an association unitthat sores the acquired unique information of the product and the acquired operation information in association with each other.

Manufacturing management method

Growing community of inventors

Tokyo, Japan

Hiroyoshi Miyano