Aisin Corporation

Kyushu Institute of Technology

Equos Research Co., Ltd.

Keio University

Kazuhiro Kuno

Hideo Yamada

Shuichi Enokida

Masatoshi Shibata

Hakaru Tamukoh

Yuji Suzuki

Shunichi Sekiguchi

Ayanori Yorozu

Shiryu Ooe

Yutaka Watanabe

Tatsuya Hashimoto

Hiromichi Ohtsuka

A mobile body control device includes: an observation trajectory acquisition unit that acquires, a trajectory along which the subject walks; a basic trajectory acquisition unit that acquires, a basic trajectory that matches the observation trajectory; a subject predicted trajectory acquisition unit that acquires a trajectory along which the subject walks after the current time point based on the specific basic trajectory; a prediction reliability calculation unit that calculates a prediction reliability based on uncertainty added along the subject predicted trajectory; a target trajectory acquisition unit that acquires, a trajectory formed by a target position of a mobile body set ahead of the subject in a traveling direction of the subject; and a control-command-sequence-generating unit that generates a control command sequence using an evaluation function that uses as input the subject predicted trajectory, prediction reliability, target trajectory, and a control trajectory.

Mobile body control device, mobile body control program, and mobile body

According to a moving body, position and direction of a user are calculated from distance measurement data, and on the basis thereof, the position and direction of the moving body are calculated. If the moving body is located in the first or fourth quadrant of the user coordinate system, a right-side moving target is set as the moving target. If the moving body is in the second or the third quadrant of the user coordinate system, a left-side moving target is set as the moving target. Accordingly, the moving target is set on the basis of the position of the moving body with respect to the user, so the moving body is prevented from cutting across in front of the user while its travelling.

Moving body

An image processing device has a function for plotting a luminance gradient co-occurrence pair of an image on a feature plane and applying an EM algorithm to form a GMM. The device learns a pedestrian image and creates a GMM, subsequently learns a background image and creates a GMM, and calculates a difference between the two and generates a GMM for relearning based on the calculation. The device plots a sample that conforms to the GMM for relearning on the feature plane by applying an inverse function theorem. The device forms a GMM that represents the distribution of samples at a designated mixed number and thereby forms a standard GMM that serves as a standard for image recognition. When this mixed number is set to less than a mixed number designated earlier, the dimensions with which an image is analyzed are reduced, making it possible to reduce calculation costs.

Image processing device, image recognition device, image processing program, and image recognition program

An image processing device converts an image that is a recognition object image to high-resolution, medium-resolution, and low-resolution images. The device sets the pixel of interest of the high-resolution image, and votes the co-occurrence in a gradient direction with offset pixels, the co-occurrence in the gradient direction pixels in the medium-resolution image, and the co-occurrence in the gradient direction pixels in the low-resolution image, to a co-occurrence matrix. The device creates such a co-occurrence matrix for each pixel combination and for each resolution. The device executes the process on each of the pixels of the high-resolution image, and creates a co-occurrence histogram wherein the elements of a plurality of co-occurrence matrices are arranged in a line. The device normalizes the co-occurrence histogram and extracts, as a feature quantity of the image, a vector quantity having as a component a frequency resulting from the normalization.

Image feature amount output device, image recognition device, the image feature amount output program, and image recognition program

During movement control wherein a moving body tracks a user while in front of the user, a coordinate obtained from the torso of the user is used as the X coordinate of the position of the user, and among the position of the body of the user and the position of the feet of the user, the position which is closer to the moving body is used as the Y coordinate. Thus, the moving body is able to move to a target position in the left/right direction (the X coordinate) and the front/back direction (the Y coordinate) that is appropriate with respect to the user. Accordingly, the distance between the moving body and the user can be maintained appropriately, and movement control in front of the user and that is not an impediment to the user can be accomplished.

A hardware configuration is constructed for calculating at high speed the co-occurrence of luminance gradient directions between differing resolutions for a subject image. In an image processing device, a processing line for high-resolution images, a processing line for medium-resolution images, and a processing line for low-resolution images are arranged in parallel, and the luminance gradient directions are extracted for each pixel simultaneously in parallel from images having the three resolutions. Co-occurrence matrix preparation units prepare co-occurrence matrices by using the luminance gradient directions extracted from these images having the three resolutions, and a histogram preparation unit outputs a histogram as an MRCoHOG feature amount by using these matrices. To concurrently processing the images having the three resolutions, high-speed processing can be performed, and moving pictures output from a camera can be processed in real time.

Method and device to determine image features using multi-resolution gradient analysis

An image recognition device sets an overall observation region which surrounds a whole body of an object and partial observation regions which surround characteristic parts of the object respectively to locations in an image which are estimated to include captured images of the object. The device clips images in the overall observation region and the partial observation regions, and calculates similarity degrees between them and previously learned images on the basis of a combination of two image feature amounts. The device calculates an optimum ratio in combining the HOG feature amount and the color distribution feature amount individually for the regions. This ratio is determined by setting a weight parameter &#x3b1;i for setting a weight used for combining the HOG feature amount and the color distribution feature amount to be included in a state vector and subjecting the result to complete search by a particle filter.

Image recognition by setting a ratio of combining the feature correlation coefficients for the respective acquired image feature amounts

An image recognition device adds a margin region filled with predetermined image data to a periphery of a captured image captured by a camera to create an expanded image larger than the captured image. When a person is too close to the camera, a part of a person image protrudes from the captured image, but a large detection window which also includes the margin region is set, whereby a window image including a protruding region is taken out of the expanded image. The window image lacks a protruding part of the person image, but it is an image showing an entire body of the person image. The image recognition device stores many reference images assuming various states of a person. The image recognition device extracts features from the window image, and compares them with a feature of the reference image, thereby recognizing the person image.

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Tokyo, Japan

Kazuhiro Kuno