Logitech Europe S.a.

Scandit Ag

Fabian Nater

Bernd Schoner

Gaurav Bradoo

Christian Floerkemeier

Aaron D Standridge

Matthias Bloch

Helmut Grabner

Vincent Borel

Kimmo Roimela

Rahim Ka

Tingshan Gou

Image analysis is used to provide label verification. An image of a label within a scene is acquired. The label includes a barcode and characters. The barcode on the label is decoded to receive an identity of an object. Optical character recognition is performed to recognize the characters on the label. Recognized characters are compared to information about decoded barcode to verify the label is correct.

Label verification using image analysis

Image analysis is used to map objects in an arrangement. For example, images of a retail shelf are used to map items for sale on the retail shelf A first vector can used to identify a relative position of a first item on a shelf to a shelving diagram, and a second vector can be used to identify a relative position of a second on the shelf to the shelving diagram, using locations of optical codes (e.g., barcodes). Absolute positions can be calculated. In some configurations, multiple images having different fields of view are matched to an overview image.

Image analysis for mapping objects in an arrangement

A method of operating a video camera system comprises recording video frames of a scene that includes a zone defined as an area within a field-of-view of the video capture element; performing a first analysis of recorded video frames showing the zone, determining, based on the first analysis, a first difference of composition thereof; performing a second analysis of an area outside of the zone; determining, based on the second analysis, a second difference of composition thereof; characterizing one or more properties corresponding to the second difference in composition; determining whether the one or more properties are associated with the first difference in composition within the zone and if so, characterizing the first difference in composition within the zone based on the one more properties and performing an action based on the characterization of the first difference in composition within the zone based on the one or more properties.

Video content activity context and regions

Images of optical codes are mapped to an overview image to localize optical codes within a space. By localizing optical codes, information about locations of various products can be ascertained. One or more techniques can be used to map the images of optical codes to the overview image. The overview image can be a composite image formed by stitching together several images.

Mapping optical-code images to an overview image

Image analysis is used to track multiple optical patterns, such as barcodes. Many applications are becoming web-based. However, web-based applications can have less computational resources than a native application. For example, a native application can be used to track barcodes based on decoding barcodes from a plurality of images. However, decoding barcodes can be computationally intense and cause lag when moved to a web-based platform. To reduce computational resources used for decoding barcodes, barcodes are tracked in several frames and decoded only periodically for a web-based application used to decode barcodes. Positions of barcodes can be tracked relative to each other to form a digital shelf. The digital shelf can be used to visualize a state of a shelf.

Image analysis for tracking, decoding, and positioning multiple optical patterns

A method for determining and recommending video content activity regions of a scene in a recorded video includes: extracting an image of a scene from a video; performing static analysis of the extracted image; detecting motion in the scene based on motion detection signals received from one or more devices configured to detect motion; performing motion analysis on the video and the motion detection signals; and based on results of the static analysis and the motion analysis, recommending via a user interface displayed on a display device a zone of interest within the scene.

Video content activity regions

In one embodiment, captured video summaries are tagged with metadata so the videos can be easily searched. The videos are classified into different scenes, depending on the type of action in the video, so searching can be based on the type of scene. In one embodiment, tags are provided for moving objects or people. The type of object that is moving is tagged (car, ball, person, pet, etc.). Video search results are ranked based on the weighting of the video events or video summaries. The video summary weighting focuses on important events, with multiple videos/images over a period of time condensed into a short summary video. This creates a weighted video summary with different time-lapse speeds that focuses on important events.

Video searching for filtered and tagged motion

In one embodiment of the present invention, a video camera selectively streams to a remote server. Still images or short video events are intermittently transmitted when there is no significant motion detected. When significant motion is detected, video is streamed to the remote server. The images and video can be higher resolution than the bandwidth used, by locally buffering the images and video, and transmitting it at a lower frame rate that extends to when there is no live streaming. This provides a time-delayed stream, but with more resolution at lower bandwidth.

Temporal video streaming and summaries

In one embodiment, the User Interface (UI) provides a live stream from the webcam, with markers on the side indicating the stored, detected important events (such as by using a series of bubbles indicating how long ago an event occurred). The indicators are marked to indicate the relative importance, such as with color coding. Upon selection of an indicator by the user, the time-lapse summary is displayed, along with a time of day indication. Alternately, the user can select to have a time-lapse display of all the events in sequence, using a more condensed time lapse, with less important events having less time or being left out.

Growing community of inventors

Zurich, Switzerland

Fabian Nater