Nec Corporation

Canon Kabushiki Kaisha

Fujikura Limited

Elpida Memory, Inc.

Fujikura Kasei Co., Ltd.

Other

Cabot Corporation

Nec Solution Innovators, Ltd.

Ahresty Corporation

Infocom Corporation

Katsuhiko Takahashi

Ryoma Oami

Hiroo Ikeda

Takuya Ogawa

Ryo Kawai

Junko Nakagawa

Kosuke Yoshimi

Yutaka Kurabayashi

Masashi Ogasawara

Hiroshi Yamada

Yuusuke Konishi

Mikio Sanada

Yusuke Konishi

Tomonari Watanabe

Yukie Ebiyama

Tetsuo Inoshita

Yuichi Nakatani

Yui Tokuda

Katsuhiro Shirota

Gaku Nakano

Asuka Ishii

Shinichi Sato

Shinya Mishina

Yoshihisa Takizawa

Shinichi Tochihara

Takayuki Imai

Masafumi Ito

Masao Kato

Kentaro Yano

Toshiharu Inui

Kenichi Saito

Tomonori Sato

Shinji Takagi

Yoichi Takada

Daisuke Nishiwaki

Tetsuhito Ikeda

Eriko Ono

Masanori Endo

Yui Kitamura

Hiroaki Izumi

Shiro Nakayama

Toshiyuki Honda

Satoshi Kunimura

Yoshiyuki Imanaka

Ichiro Saito

Koji Ito

Takashi Shibata

Makoto Shioya

Akio Kashiwazaki

Hiroyoshi Miyano

Mamoru Sakaki

Yoshifumi Hattori

Masato Katayama

Muga Mochizuki

Hiroshi Tomioka

Soma Shiraishi

Keiichi Murai

Mayumi Yamamoto

Takami Sato

Eiichi Suzuki

Toshifumi Nakajima

Kenichi Moriya

Shin-ichi Sato

Shigemi Takahashi

Yu Nabeto

Kazuya Tanaka

Makoto Terao

Mifune Hirose

Masao Akutsu

Azusa Sawada

Yuko Nishioka

Katsumi Morikawa

Ryosuke Sakai

Yuzo Yamamoto

Yukihiko Kurosawa

Akinobu Ono

Toshio Ochiai

Kiwako Ohmori

Toru Kubokawa

Tomokazu Kaneko

Masateru Mori

Kazunori Tobita

Kouji Okamoto

Toshimitu Hirayama

Yutaka Kurbayashi

Hikaru Yasuhara

Koji Okamoto

Takahumi Hikima

Hiroaki Zaima

In an object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists for each partial region set with respect to inputted image data. The weight computation unit uses weight computation parameters to compute a weight for each of the plurality of object detection units on a basis of the image data and outputs of the plurality of object detection units, the weight being used when the scores outputted by the plurality of object detection units are merged. The merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. The first loss computation unit computes a difference between a ground truth label of the image data and the score merged by the merging unit as a first loss. Then, the first parameter correction unit corrects the weight computation parameters so as to reduce the first loss.

Object detection device, learned model generation method, and recording medium

The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.

Information processing device, learning method, and recording medium

In an object detection device, a plurality of object detection units output a score indicating probability that a predetermined object exists, for each partial region set to image data inputted. The weight computation unit computes weights for merging the scores outputted by the plurality of object detection units, using weight calculation parameters, based on the image data. The merging unit merges the scores outputted by the plurality of object detection units, for each partial region, with the weights computed by the weight computation unit. The target model object detection unit configured to output a score indicating probability that the predetermined object exists, for each partial region set to the image data. The first loss computation unit computes a first loss indicating a difference of the score of the target model object detection unit from a ground truth label of the image data and the score merged by the merging unit. The first parameter correction unit corrects parameters of the target model object detection unit to reduce the first loss.

Object detection device, learning method, and recording medium

An information processing apparatus () includes a first analyzing unit (), a second analyzing unit (), and an estimating unit (). The first analyzing unit () calculates a flow of a crowd in a capturing range of a fixed camera () using a first surveillance image (). The second analyzing unit () calculates a distribution of an attribute of objects in a capturing range of a moving camera () using a second surveillance image (). The estimating unit () estimates an attribute distribution for a range that is not included in the capturing range of the moving camera ().

Information processing apparatus, control method, and program

An image acquisition unitacquires a plurality of images. The plurality of images include an object to be inferred. An image cut-out unitcuts out an object region including the object from each of the plurality of images acquired by the image acquisition unit. An importance generation unitgenerates importance information by processing the object region cut out by the image cut-out unit. The importance information indicates the importance of the object region when an object inference model is generated, and is generated for each object region, that is, for each image acquired by the image acquisition unit. A learning data generation unitstores a plurality of object regions cut out by the image cut-out unitand a plurality of pieces of importance information generated by the importance generation unitin a learning data storage unitas at least a part of the learning data.

Generating learning data from important, cut-out object regions

The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

Helmet wearing determination method, helmet wearing determination system, helmet wearing determination apparatus, and program

In the object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists, for each partial region set with respect to image data inputted. The weight computation unit computes a weight for each of the plurality of object detection units by using weight computation parameters and based on the image data. The weights are used when the scores outputted by the plurality of object detection units are merged. The weight redistribution unit changes the weight for a predetermined object detection unit, among the weights computed by the weight computation unit, to 0 and output the weights. The merging unit merges the scores outputted by the plurality of object detection units for each of the partial regions, by using the weights computed by the weight computation unit and including the weight changed by the weight redistribution unit. The loss computation unit computes a difference between a ground truth label of the image data and the merged score merged by the merging unit as a loss. Then, the parameter correction unit corrects the weight computation parameters so as to reduce the loss.

An information processing apparatus () includes an event detection unit (), an input reception unit (), and a processing execution unit (). The event detection unit () detects a specific event from video data. The input reception unit () receives, from a user, input for specifying processing to be executed. The processing execution unit () executes first processing specified by input received by the input reception unit (), and executes second processing of generating learning data used for machine learning and storing the generated learning data in a learning data storage unit (). The processing execution unit () discriminates, in the second processing, based on a classification of the first processing specified by input received by the input reception unit (), whether a detection result of a specific event is correct, and generates learning data including at least a part of video data, category information indicating a category of a specific event detected by the event detection unit (), and correct/incorrect information indicating whether a detection result of an specific event is correct or incorrect.

Information processing apparatus, information processing method, and program

The first parameter generation unitgenerates a first parameter, which is a parameter of a first recognizer, using first learning data including a combination of data to be recognized, a correct label of the data, and domain information indicating a collection environment of the data. The second parameter generation unitgenerates a second parameter, which is a parameter of a second recognizer, using second learning data including a combination of data to be recognized that is collected in a predetermined collection environment, a correct label of the data, and target domain information indicating the predetermined collection environment, based on the first parameter. The third parameter generation unitintegrates the first parameter and the second parameter to generate a third parameter to be used for pattern recognition of input data by learning using the first learning data.

Pattern recognition system, parameter generation method, and parameter generation program

An object detection apparatusis provided with: a fish-eye image acquisition unitconfigured to acquire a time series fish-eye image; a horizontal panorama image generation unitconfigured to, for each frame, perform conversion to a horizontal panorama image in which a vertical direction in a real space is expressed in a perpendicular direction of the frame, and an azimuth is expressed equiangularly in a horizontal direction of the frame; an edge pair extraction unitconfigured to extract a pair of edges in the perpendicular direction from the horizontal panorama image; a change rate extraction unitconfigured to extract a change rate of an inter-edge distance between the pair of edges; a lower end region extraction unitconfigured to extract a region of a lower end of an object providing the pair of edges; a distance change rate extraction unitconfigured to calculate a distance from the object to the fish-eye camera based on the position of the region of the lower end of the object in the horizontal panorama image, and extract a change rate of the distance; and an object detection unitconfigured to determine whether or not the object exists based on the change rate of the inter-edge distance and the change rate of the distance.

Object detection apparatus, object detection method, and computer-readable recording medium

The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit () transforms a plurality of images acquired by an image acquisition unit (). A reliability level calculation unit () calculates a level of reliability representing how small the misalignment between images is. A score calculation unit () calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (), and the level of reliability calculated by the reliability level calculation unit (). An object detection unit () detects an object in the images on the basis of the total score.

Object detection device, object detection system, object detection method, and recording medium having program recorded thereon

Growing community of inventors

Tokyo, Japan

Katsuhiko Takahashi