International Business Machines Corporation

Udisense Inc.

Mellanox Technologies, Ltd.

Dror Porat

Daniel Nechemia Rotman

Udi Barzelay

Tal Hakim

Mattias Marder

Gal Ashour

Amnon Karni

Natalie Louise Barnett

Yanai Victor Ankri

Sivan Hurvitz

Dotan David Levi

Shie Mannor

Ophir Azulai

Tor Ivry

Assaf Weissman

Ohad Markus

Eyal Frishman

Gal Chechik

Yevgeny Burshtein

Assaf Glazer

Assaf Hallak

Eshed Ram

Oded Margalit

Slava Shechtman

Tal Drory

Elad Amrani

Evgeny Hazanovich

Fora Gilboa-Solomon

A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Content-aware, machine-learning-based rate control

A method for photogrammetric measurement includes capturing a sequence of images of an infant lying in a crib and processing the images so as to extract a skeleton of the infant from one or more of the images. A height of the infant is estimated based on the extracted skeleton.

Infant height estimation

A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Quality-metric-agnostic rate control

A method for photogrammetric measurement includes providing a bedsheet having one or more patterns printed thereon in accordance with a pattern template, which defines respective locations of the one or more patterns in a template coordinate frame. An image is received, in an image coordinate frame, of a person lying on a bed, which is covered by the bedsheet. The image is processed in order to identify the one or more patterns in the image and to match the one or more patterns identified in the image to the one or more patterns in the pattern template. A transformation is computed, based on the matched patterns, between the image coordinate frame and the template coordinate frame. A dimension of the person is measured by applying the computed transformation to the image of the person.

Measurement calibration using patterned sheets

A processor may receive a query from a user. The processor may identify one or more subjects in the query. The one or more subjects may include a particular subject. The processor may identify one or more sections of a recording. The processor may group the one or more sections into one or more subdivisions. The processor may determine that the particular subject is in at least one of the one or more sections. The processor may display the one or more subdivisions of the one or more sections that include the particular subject to the user.

Query-based granularity selection for partitioning recordings

Bedsheet

Embodiments may provide techniques that provide identification of images that can provide reduced resource utilization due to reduced sampling of video frames for visual recognition. For example, in an embodiment, a method of visual recognition processing may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: coarsely segmenting video frames of video stream into a plurality of clusters based on scenes of the video stream, sampling a plurality of video frames from each cluster; determining a quality of each cluster, re-clustering the video frames of video stream to improve the quality of at least some of the clusters.

System and method for reducing resources costs in visual recognition of video based on static scene summary

Automated detection and approximation of objects in a video, including: (a) sampling a provided digital video, to obtain a set of sampled frames; (b) applying an object detection algorithm to the sampled frames, to detect objects appearing in the sampled frames; (c) based on the detections in the sampled frames, applying an object approximation algorithm to each sequence of frames that lie between the sampled frames, to approximately detect objects appearing in each of the sequences; (d) applying a trained regression model to each of the sequences, to estimate a quality of the approximate detection of objects in the respective sequence; (e) applying the object detection algorithm to one or more frames in those of the sequences whose quality of the approximate detection is below a threshold, to detect objects appearing in those frames.

Automated detection and approximation of objects in video

An example system includes a processor to receive a number of video frames. The processor is to apply a grid to each video frame of the number of video frames and generate features for each cell in the grid. The processor is to calculate distances between matching regions of two consecutive frames. The processor is to apply max pooling followed by min pooling in horizontal regions of the number of video frames. The processor is to apply max pooling followed by min pooling in vertical regions of the number of video frames. The processor is to detect an application switch in response to detecting both a horizontal change and a vertical change between the two consecutive video frames exceed a threshold.

Detecting application switches in video frames using min and max pooling

A system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, video data, perform a first tracking, using a tracking algorithm, of an identified target in a sequence of frames of said video data, wherein said first tracking is performed forward in time, perform a second tracking, using said tracking algorithm, of said target in said sequence, wherein said second tracking is performed backward in time, and calculate a confidence score for a tracked location of said target in a frame of said sequence, based, at least in part, on a comparison between said first tracking and said second tracking.

Detection of visual tracker divergence

An example system includes a processor to receive detections or recognitions with confidence scores for an object in a medium from a plurality of trained detection or recognition models. The processor is to generate a probability of correctness for each of the detections or recognitions based on the confidence scores via correctness mappings generated for each of the trained detection or recognition models. The processor is to also select a detection or recognition with a higher probability of correctness from the detections or recognitions. The processor is to perform a detection or recognition task based on the selected detection or recognition.

Selecting object detections or recognitions using correctness mappings

A computer-implemented method for providing a graphical user interface (GUI) for video navigation by a user, including generating a GUI comprising a representation of partitioned segments of video content, the representation depicting a dimension of time, a dimension of scale, and a dimension of relevance for the partitioned segments of video content, wherein the GUI includes a first axis that depicts the dimension of time and a second axis that depicts the dimension of scale. The method further entails determining the dimension of relevance for the partitioned segments of video content based on receiving a text query from a user dynamically, or from a pre-defined source, and comparing one or more attributes of the partitioned segments of video content to the text query, or to the pre-defined source. The method further includes assigning a color to the partitioned segments of video content according to a color scheme for easy navigation.

Generating a graphical user interface to navigate video content

A method for previewing a data stream, comprising: extracting multiple samples from a data stream at multiple different sampling rates, wherein each sampling rate corresponds to a different relevance score associated with a different portion of the data stream; using the multiple extracted samples to create a representation of the data stream comprising multiple thumbnails, wherein the representation is weighted in accordance with the multiple different relevance scores; mapping, in accordance with a relevance based mapping scheme, the representation onto a gauge provided with the data stream; and displaying one of the multiple thumbnails in accordance with the relevance based mapping scheme, responsive to detecting a navigation indication on the gauge.

Instantaneous preview of a data stream with non-linear time warping

A system comprising a non-transient computer-readable storage medium having stored thereon instructions and at least one hardware processor configured to execute the instructions, to receive a video sequence; divide the video sequence into one or more scenes based on scene boundaries, wherein each scene comprises a plurality of temporally-contiguous image frames, and wherein said scene boundaries are being determined based on a similarity metric between two temporally-contiguous image frames meeting a dissimilarity threshold; and, for each scene of the one or more scenes, (i) generate a plurality of preliminary classifications of an object appearing in at least some of said image frames in the scene, wherein each of said plurality of preliminary classifications has a confidence score, and (ii) calculate a combined classification of the object based on said plurality of preliminary classifications, wherein each of said preliminary classifications is weighted in accordance with its confidence score.

Object recognition in video

Segmenting serial data by processing multiple candidate segmentation point sets associated with a serial data set, each candidate set including a different number of candidate segmentation points representing the serial data set as segments in accordance with segmentation criteria, where the processing includes determining, for each of the candidate points, a count of the candidate sets that include the candidate point, and creating, for each of the candidate sets, a corresponding alternative segmentation point set associated with the serial data set, where the alternative set includes n alternative segmentation points representing the serial data set as n+1 segments, where n equals the number of candidate points in the candidate set to which the alternative set corresponds, and where the n alternative points in the alternative set correspond to n of the candidate points having the greatest counts, and arranging the alternative sets in order of their numbers of segmentation points.

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Haifa, Israel

Dror Porat