Omron Corporation

Koichi Kinoshita

Hitoshi Mukai

Ambrish Tyagi

John Drinkard

Tomoyoshi Aizawa

Yoshiharu Tani

Tomohiro Yabuuchi

Tadashi Hyuga

Miki Matsuoka

Hatsumi Aoi

Shigenori Nagae

Shuichiro Tsukiji

Yamato Takeuchi

Ayumi Takemoto

Mei Uetani

Takayoshi Yamashita

Masaki Suwa

Kazuo Yamamoto

Manabu Kawashima

The purpose of the present invention is to provide a gaze detector with which it is possible to detect a gaze (start point and gaze direction) by a simple configuration. The gaze detector detects a person's gaze, wherein the gaze detector is provided with a camera unitfor imaging the face of the person, a projector unitfor projecting a prescribed pattern light to the face of the person, a control unitfor controlling the presence of projection of the pattern light by the camera unitand the projector unit, and a gaze detection processing unitfor detecting a gaze from the image of the face of the person imaged by the camera unit.

Gaze detector, method for controlling gaze detector, method for detecting corneal reflection image position, and storage medium

An image processing apparatus includes a first learner trained to detect an orientation of a face in an image including a human face for receiving an input of a target image including a human face and outputting first information associated with an orientation of a face included in the target image, at least one second learner trained, for the first information, to detect a position of a facial component in an image including a human face for receiving an input of the target image and outputting second information associated with a position of a facial component included in the target image, and a control unit that controls the first learner and the second learner. The control unit uses the first learner to obtain the first information from the target image, and uses the second learner corresponding to the first information to obtain the second information from the target image.

Image processing apparatus with learners for detecting orientation and position of feature points of a facial image

An image analyzer repeatedly obtains a captured image including a subject to be detected, and detects, in the captured image, a first area likely to include the subject without a cover, detects, in the captured image, a second area likely to include the subject with a cover, and determines whether the subject is covered by the cover based on a detection result of the first area and a detection result of the second area and a determination result for the captured image obtained previously.

Image analyzer, image analysis method, and image analysis program

A data processing device that performs data processing of monitoring a person, the data processing device includes: a calculator configured to calculate pupil movement and head movement of the person; an evaluator configured to evaluate a suitability degree of a situation in calculating vestibulo-ocular reflex movement based on the pupil movement and the head movement of the person; a provision unit configured to provide the suitability degree evaluated by the evaluator to data relating to the pupil movement and the head movement of the person calculated by the calculator; and a reflex movement calculator configured to calculate the vestibulo-ocular reflex movement of the person based on the data in consideration of the suitability degree.

Data processing device, monitoring system, awakening system, data processing method, and data processing program

A data processing device that performs data processing of monitoring a person, the data processing device includes: a calculator configured to calculate pupil movement and head movement of the person; an evaluator configured to evaluate a suitability degree of a situation in calculating the VOR; a provision unit configured to provide the suitability degree evaluated by the evaluator to data calculated by the calculator; a selector configured to select a first technique of calculating the sleepiness based on the VOR of the person or a second technique different from the first technique based on the suitability degree provided to the data; a first sleepiness calculator configured to calculate the sleepiness based on the first technique when the selector selects the first technique; and a second sleepiness calculator configured to calculate the sleepiness based on the second technique when the selector selects the second technique.

A device configured to apprehend a road surface shape by using data measured by a measuring device appropriately apprehends a road surface shape. A road surface shape measuring device is provided with a road surface information acquiring unit and a road surface shape determining unit. The road surface information acquiring unit acquires road surface information about each of multiple points on a road surface, the road surface information including: a road surface position which is the position, of the road surface, on a reference plane divided into a plurality of sectioned regions; and a road surface height which is the height of the road surface at the road surface position. The road surface shape determining unit estimates respective surface shapes of the sectioned regions by using the road surface information included in the corresponding sectioned regions, and thereby determines a partial shape or the entire shape of the road surface.

Road surface shape measuring device, measuring method, and non-transitory computer-readable medium

According to one aspect of the teachings presented herein, a machine vision system includes one or more sensor units that are each advantageously configured to use different pairings among a set of spaced-apart image sensors, to provide redundant object detection for a primary monitoring zone, while simultaneously providing for the detection of objects that may shadow the primary monitoring zone. Further, a plurality of 'mitigations' and enhancements provide safety-of-design and robust operation. Such mitigations and enhancements include, for example, bad-pixel detection and mapping, cluster-based pixel processing for improved object detection, test-image injection for fault detection, dual-channel, redundant processing for safety-critical object detection, temporal filtering to reduce false detections, and the use of high dynamic range (HDR) images, for improved operation over variable ambient lighting conditions.

Method and apparatus for projective volume monitoring

According to one aspect of the teachings presented herein, a projective volume monitoring apparatus is configured to detect objects intruding into a monitoring zone. The projective volume monitoring apparatus is configured to detect the intrusion of objects of a minimum object size relative to a protection boundary, based on an advantageous processing technique that represents range pixels obtained from stereo correlation processing in spherical coordinates and maps those range pixels to a two-dimensional histogram that is defined over the projective coordinate space associated with capturing the stereo images used in correlation processing. The histogram quantizes the horizontal and vertical solid angle ranges of the projective coordinate space into a grid of cells. The apparatus flags range pixels that are within the protection boundary and accumulates them into corresponding cells of the histogram, and then performs clustering on the histogram cells to detect object intrusions.

A tracking apparatus and method in which the possible position of a current face is estimated based on the face positions previously determined as well as plural pieces of ambient information corresponding to the possible positions obtained. The current face position is estimated based on the position of the ambient information that is most similar to the ambient information obtained from previous detections.

Tracking apparatus that tracks a face position in a dynamic picture image using ambient information excluding the face

Plural nodes are arranged at predetermined initial positions, and feature values at plural sampling points around each node are obtained as a node feature value of each corresponding node. An error estimator indicating displacement between the current position of each node and the position of corresponding feature point is obtained based on correlation information on a difference between the node feature value obtained in a state in which the plural nodes are arranged at correct positions of the corresponding feature points and the node feature value obtained in a state in which the plural nodes are arranged at wrong positions of the corresponding feature points in a learning image, correlation information on a difference between the correct position and the wrong position, and a node feature value of each node. The position of each feature point is estimated in an input image based on the error estimator and the current position of each node.

Face feature point detection apparatus and feature point detection apparatus

A device and method for detecting feature points of an object from an image. A three-dimensional model is created in which a plurality of nodes corresponding to feature points in a learning image are defined. The model is projected onto an input image and a feature value is derived from a plurality of sampling points around a projection point of each node. An error estimated amount is computed based on the displacement of a feature point between a correct model and an error model. The three dimensional position of each feature point in the input image is estimated based on the error estimated amount and a three dimensional model.

Feature point detecting device, feature point detecting method, and feature point detecting program

An object determining device for selecting a target face for processing from plural faces in an image, includes a face detecting unit that detects faces from an image; a face information recording unit that records the face detected in the past by the face detecting unit and the detection history relating to this detection as associated with each other; and a face selecting unit that selects a target face for processing, from the faces included in the image based upon the detection history. The object determining device includes a face information updating unit that, in a case where the face detected by the face detecting unit is not present in the face information recording unit, newly registers the face, and in a case where the face is present, updates the face information.

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Kyoto, Japan

Koichi Kinoshita