Abstract: 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.

Abstract: 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.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: One or more computing devices, systems, and/or methods for determining expected revenue thresholds for presentation of content items via client devices are provided. During a first period of time, whether to present a first content item via a first client device may be determined based upon a comparison of a first expected revenue associated with the first content item with a first expected revenue threshold. A first revenue value associated with presentation of content items via client devices within the first period of time may be determined. The first expected revenue threshold may be modified to generate a second expected revenue threshold based upon the first revenue value and a target revenue value. Whether to present a second content item via a second client device may be determined based upon a comparison of a second expected revenue associated with the second content item with the second expected revenue threshold.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.