International Business Machines Corporation

Other

Toshiba Global Commerce Solutions Holdings Corporation

Norman C Haas

Sharathchandra UmapathiRao Pankanti

Ying Li

Arun Hampapur

Jonathan Hudson Connell, Ii

Charles A Otto

Myron Dale Flickner

Hoang Trinh

Rudolf Maarten Bolle

Jonathan H Connell

Rakesh Mohan

Gabriel Taubin

Robert Stephen Olyha, Jr

Frank J Oles

Tong Zhang

Etienne Marcheret

Chalapathy Venkata Neti

Sharathchandra Umapathirao Pankanti

Yuichi Fujiki

Gerasimos Potamianos

Yashodhara M Patnaik

Balamanohar Paluri

Bernadette R Duponchel

Unsang Park

Yuichi Fujiji

Thomas J Hammershoy

Weiguang Guan

A system and computer program product for tracking and monitoring assets along a transport route. The system includes at least one receiver for receiving asset identifications transmitted from the assets, where each asset transmits its own asset identification. The receiver also receives physical location coordinates of each of the assets. A plurality of cameras is dispersed along the transport route for transmitting camera images of the assets. The system further includes a server coupled to the receiver and cameras. The server is configured to recognize the assets in the camera images, to correlate the asset identification from the receiver with recognized assets in the camera images, and to correlate the physical location coordinates of each of the assets with physical location coordinates of the cameras. The system monitors the visual appearance of the assets, and keeps track of whether or not they have sustained physical damage.

Asset tracking and monitoring along a transport route

Video image data is acquired from synchronized cameras having overlapping views of objects moving past the cameras through a scene image in a linear array and with a determined speed. Processing units generate one or more object detections associated with confidence scores within frames of the camera video stream data. The confidence scores are modified as a function of constraint contexts including a cross-frame constraint that is defined by other confidence scores of other object detection decisions from the video data that are acquired by the same camera at different times; a cross-view constraint defined by other confidence scores of other object detections in the video data from another camera with an overlapping field-of-view; and a cross-object constraint defined by a sequential context of a linear array of the objects, spatial attributes of the objects and the determined speed of the movement of the objects relative to the cameras.

Multisensor evidence integration and optimization in object inspection

A global position of an observed object is determined by obtaining a first global position of an observed object with at least one positioning device. A determination is made as to whether a set of stored visual characteristic information of at least one landmark matches a visual characteristic information set obtained from at least one captured image comprising a scene associated with the observed object. In response to the set of stored visual characteristic information matching the obtained visual characteristic information set, a second global position of the observed object is determined based on a set of stored location information associated with the at least one landmark and the first global position.

Location determination for an object using visual data

Under the present invention, item verification is automated and expedited. Specifically, items to be purchased can be scanned by the shopper using a barcode reader (e.g., a scanner), attached to or positioned near the checkout station. As items are scanned, they are identified based on their barcode, and added to an item list. Item verification can then be performed at checkout using imaging technology. Specifically, as items are scanned, an item verification unit will capture an appearance thereof (via a camera). Item verification software within the item verification unit will access a database that associates items with their images/appearances. The appearance will be compared for consistency to the identity as determined based on the scan. In general, the item verification unit is a separate unit from the cash register, but adapted to work in conjunction therewith (e.g., as a pluggable system, via wireless communication, etc.).

Visual security for point of sale terminals

An imaging system includes an image capturing device and a plurality of reflective devices. The image capturing device is configured to receive a plurality of images reflected by the plurality of reflective devices. Responsive to receiving the plurality of images, the image capturing device is further configured to capture within a single frame at least a first image corresponding to a first side of a first railroad track rail, a second image corresponding to a second side of the first railroad track rail, a third image corresponding to a first side of a second railroad track rail, and a fourth image corresponding to a second side of the second railroad track rail.

System and method for inexpensive railroad track imaging for inspection

An apparatus, computer program product, and method for reducing persistent shadows within an image. The apparatus includes a camera configured to generate frames of the image. The apparatus also includes a computer processor. The computer processor calculates the average normalized brightness for each pixel in the image and adjusts the brightness of each pixel with the average normalized brightness.

Frame to frame persistent shadow reduction within an image

The present invention provides a smart scanning system comprising an integrated scanning and image capture system in which one or more image capture device(s) (e.g., still camera, video camera, etc.) and a barcode scanner are positioned within a common enclosure that is a component of a checkout station. The barcode of item is scanned and an image of the item is recorded. The identity of the item as determined based on the barcode is compared to its appearance as determined based on its image. If the identity is inconsistent with its appearance, a discrepancy is registered. It is then determined whether the discrepancy is due to fraud (e.g., theft) or device error. In the case of the latter, the system can be updated to prevent a repeat of the error.

Smart scanning system

Under the present invention, item verification is automated and expedited. Specifically, items to be purchased can be scanned by the shopper using a barcode reader (e.g., a scanner) attached to or positioned near the shopping receptacle. As items are scanned, they are identified based on their barcode and added to an item list. Item verification can then performed at checkout using imaging technology. For example, the shopping cart or shopping basket can be brought into the field of view of a computer-connected camera. The camera and computer can, working from the customer's item list developed when the items are scanned, observe each product in the receptacle and 'ring it up'. If all products can be accounted for, the customer is free to leave; otherwise the customer is denied egress, informed of the problem, etc. A store employee can also be signaled to investigate. The total time required to make the decision is the time to take a picture and process it, which by human standards is very fast; faster than existing verification methods.

Secure self-checkout

A system, method, and computer program product for detecting anomalies in an image. In an example embodiment the method includes partitioning each image of a set of images into a plurality of image local units. The method further includes clustering all local units in the image set into clusters, and consequently assigning a class label to each local unit based on the clustering results. The local units with identical class labels having at least one substantially related image feature. Further, the method includes assigning a weight to each of the local units based on a variation of the class labels across all images in a set of images. The method further includes performing a clustering over all images in the set by using a distance metric that takes the learned weight of each local unit into account, then determining the images that belong to minorities of the clusters as anomalies.

Anomaly detection in images and videos

A method, system, and computer program product for automatically inspecting railroad tracks. The method includes assessing a configuration of rail components depicted in an image by comparing the configuration of the rail components to known hazards. The method also includes determining a severity of detected problems in the configuration of the rail components, using a computer processor.

Method and system of rail component detection using vision technology

A system and method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A given color channel of the image is extracted. At least one blob that stands out from a background of the given color channel is identified. One or more features are extracted from the blob. The one or more features are provided to a plurality of pre-learned object models each including a set of pre-defined features associated with a pre-defined blob type. The one or more features are compared to the set of pre-defined features. The blob is determined to be of a type that substantially matches a pre-defined blob type associated with one of the pre-learned object models. At least a location of an object is visually indicated within the image that corresponds to the blob.

Detection of objects in digital images

A method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A given color channel of the image is extracted. At least one blob that stands out from a background of the given color channel is identified. One or more features are extracted from the blob. The one or more features are provided to a plurality of pre-learned object models each including a set of pre-defined features associated with a pre-defined blob type. The one or more features are compared to the set of pre-defined features. The blob is determined to be of a type that substantially matches a pre-defined blob type associated with one of the pre-learned object models. At least a location of an object is visually indicated within the image that corresponds to the blob.

A method, information processing system, and computer program storage product annotate video images associated with an environmental situation based on detected actions of a human interacting with the environmental situation. A set of real-time video images are received that are captured by at least one video camera associated with an environment presenting one or more environmental situations to a human. One or more user actions made by the human that is associated with the set of real-time video images with respect to the environmental situation are monitored. A determination is made, based on the monitoring, that the human driver has one of performed and failed to perform at least one action associated with one or more images of the set of real-time video images. The one or more images of the set of real-time video images are annotated with a set of annotations.

Growing community of inventors

Overland Park, KS, United States of America

Norman C Haas