Sony Corporation

Sony Group Corporation

Hiroaki Nishimura

Nikolaos Georgis

James Kuch

Kazuaki Taguchi

Implementations generally relate to estimating grouped observations. In some implementations, a method includes detecting a license plate of a vehicle. The method further includes capturing a plurality of images of the license plate. The method further includes computing candidate regions associated with the license plate based on the images of the license plate. The method further includes computing candidate characters of the license plate based on the candidate regions and the images of the license plate. The method further includes selecting target characters from the candidate characters based on one or more predetermined selection criteria.

Bidirectional statistical consolidation of multi-modal grouped observations

An information processing apparatus includes an entry and leaving management unit configured to analyze a captured image and detect vehicle information included in the captured image, and in a case where there is reservation information of parking corresponding to the detected vehicle information, open a gate of a parking lot corresponding to the reservation information.

Information processing apparatus and method, and program

An information processing apparatus includes a specifying unit configured to specify, on the basis of a position of an object in a captured image, from a plurality of control targets, the control target to be controlled in accordance with the object.

Apparatus for controlling multiple gates through which vehicles travel

An electronic apparatus and method for license plate recognition-based vehicle control is provided. The electronic apparatus controls at least one of a plurality of image capture devices to capture one or more images of one or more second vehicles different from a first vehicle. The electronic apparatus detects a license plate of a second vehicle of the one or more second vehicles in the captured one or more images. The electronic apparatus further determines a depth map of the detected license plate of the second vehicle of the one or more second vehicles with respect to the first vehicle. The electronic apparatus further detects one or more events related to the second vehicle based on the determined depth map and controls one or more operations of the first vehicle based on the detected one or more events related to the second vehicle.

License plate recognition based vehicle control

An electronic apparatus and method for traffic speed enforcement based on license plate detection is provided. The electronic apparatus controls an image-capture device on a first vehicle to capture a sequence of images of a second vehicle and detects a license plate of the second vehicle in the captured sequence of images. The electronic apparatus extracts a set of license plate images from a set of images in the sequence of images and determines a first change in a size of a first license plate image with respect to that of a second license plate image. The electronic apparatus further determines a speed of the first vehicle and a traffic speed violation by the second vehicle based on the determined first change and the determined speed of the first vehicle. The electronic apparatus further controls a media device to output a notification indicative of the determined traffic speed violation.

Traffic speed enforcement based on license plate detection

An image-capture apparatus and method for an artificial intelligence (AI) based control of imaging parameters of the image-capture apparatus is provided. The image-capture apparatus controls the imaging sensor based on a set of imaging parameters associated with the imaging sensor, to acquire imaging information. The acquired imaging information includes a first object of a plurality of objects. The image-capture apparatus generates by, a neural network model, a first classification result based on the acquired imaging information and modifies one or more first imaging parameters of the set of imaging parameters based on the generated first classification result for the first object. The image-capture apparatus further controls the imaging sensor based on the modified set of imaging parameters, to reacquire the imaging information to maximize a confidence of the neural network model for the detection of the first object in the reacquired imaging information.

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Paramus, NJ, United States of America

Hiroaki Nishimura