Abstract: Aspects described herein provide a computer-implemented method and system for generating topographic map data at different scales. More specifically, the topographic map data is reduced from large scale to small scale, wherein the scale of the vector features is reduced according to their geometry, feature type and attributes. In order to quickly and easily output digital maps at different scales so that a user may quickly zoom in and out of a map, different zoom levels are produced for different scales, each zoom level containing a variable amount of detail according to its scale, with larger scale zoom levels generally containing more detail than small scale zoom levels. Each zoom level is made up of one or more vector tiles representative of a geographic area, wherein the vector tiles may contain one or more vector features that are representative of objects within that geographic area, and other information related to that geographical area.

Abstract: Techniques for prioritization of media related to an incident are provided. Confirmed incident related media may be retrieved, the confirmed incident related media having been confirmed as being associated with the incident. Artifacts of interest may be identified in the confirmed incident related media. Presence of the artifacts of interest in a plurality of received media may be determined. The plurality of received media may be prioritized based on the presence of the artifacts of interest.

Abstract: An image processing method includes defining relations between entities of a target of which a motion is to be predicted from an image of a first time point based on a feature vector of the entities, estimating a dynamic interaction between the entities at the first time point based on the defined relations between the entities, predicting a motion of the entities changing at a second time point based on the estimated dynamic interaction, and outputting a result to which the motion predicted at the second time point is applied.

Abstract: Disclosed are a pedestrian re-identification method and apparatus based on local feature attention. The method includes the following steps: S1: obtaining an original surveillance video image data set, and dividing the original surveillance video image data set into a training set and a test set in proportion; and S2: performing image enhancement on the original surveillance video image training set to obtain enhanced images, and converting the enhanced images into sequence data.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.

Abstract: Systems and methods for tracking objects though a traffic control system include an image sensor configured to capture a stream of images of scene from an associated real-world position, an object tracker configured to identify an object in the captured images and define an associated object location in the captured images, a three-dimensional stage model system configured to transform the associated object location in the image to three-dimensional coordinates associated with the image sensor, and a three-dimensional world model configured to transform identified objects to real-world coordinates. Embodiments use lens aberration, sensor mounting height and location, accelerometer, gyro-compass and/or global position satellite information to generate a situational map.

Abstract: Selections of content shared from a remote device during a video conference are copied to a destination of a computing device connected to the video conference live or at which a recording of the video conference is viewed. The content shared from the remote device during the video conference is output at a display of the computing device. A portion of the content is selected according to an instruction received from a user of the computing device while output at the display of the computing device to copy to a destination associated with software running at the computing device. The portion of the content is identified using a machine vision process performed against the content while output at the display of the computing device. The portion of the content is then copied to the destination.

Abstract: A video transmission method according to the present disclosure includes: generating a transmission signal including, in a time series, first video data having a first luminance dynamic range and second video data having a second luminance dynamic range wider than the first luminance dynamic range; and transmitting the transmission signal generated. In the generating of the transmission signal, a signal level corresponding to a luminance value is limited to a value lower than a limit value that is predetermined, in a transition period provided for switching from one of the first video data and the second video data to the other.

Abstract: A system may be configured to perform object counting in high volume traffic. In some aspects, the system may determine object detection information defining an absence of an object within the region of interest in a current video frame, and determine that a location associated with the object is within a placeholder-eligible area within the region of interest based on first tracker information determined in a previous video frame. Further, the system may determine second tracker information including a bounding representation for the object based at least in part on the location and object detection information, and determine, based on the second tracker information, an object count representing a number of the objects that have entered the region of interest and/or a number of the objects that have exited the region of interest.

Abstract: A system may be configured to perform object counting in high volume traffic. In some aspects, the system may detect a candidate object within the region of interest in a current video frame, and determine that the candidate object is a detected object based at least in part on comparing an attribute value of the candidate object to historic attribute information determined during a plurality of previous video frames. Further, the system may determine track information based on the detected object and determine an object count representing a number of the objects that have entered the region of interest and/or a number of the objects that have exited the region of interest.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: A display control device includes: an image acquisition unit configured to acquire captured image data from an imaging unit that captures an image of a peripheral area of a vehicle; a display control unit configured to cause image data based on the captured image data to be displayed on a screen; and an operation receiving unit configured to receive designation of an arbitrary point on the screen, in which the display control unit enlarges display on the screen about the designated point as a center of enlargement in a state where a display position of the designated point does not move on the screen when the operation receiving unit receives the designation of the arbitrary point on the screen.

Abstract: A method includes obtaining a reference image and a target image each representing an environment containing moving features and static features. The method also includes determining an object mask configured to mask out the moving features and preserves the static features in the target image. The method additionally includes determining, based on motion parallax between the reference image and the target image, a static depth image representing depth values of the static features in the target image. The method further includes generating, by way of a machine learning model, a dynamic depth image representing depth values of both the static features and the moving features in the target image. The model is trained to generate the dynamic depth image by determining depth values of at least the moving features based on the target image, the object mask, and the static depth image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.

Abstract: A system may generate image data of users within a facility. Such users may be part of a group that is unknown to the system. The system can predict group data for two or more users based on a resemblance or the users being within a threshold distance of each other. However, if a confidence level associated with the predicted group data is below a threshold value, the group data is deemed unreliable and assistance from an associate is deemed necessary. A user interface that includes a portion of the image data, information about the predicted group data, and other interface elements is presented to the associate via a display. Based on the input data received from the associate, the group data can be confirmed or rejected. If the group data is confirmed, an association is made between the users and a group identifier.

Abstract: A driver monitoring system includes at least two cameras configured to record images of a driver within a cabin of a vehicle. A first camera is disposed on a driver side of the cabin and a second camera is disposed on a passenger side of the cabin. At least one of the at least two cameras is disposed in a mirror replacement display or in a mirror replacement display location. A controller is in communication with the at least two cameras and is configured to determine a pose of the driver based on images from the at least two cameras. At least one of the images originates from at least one of the first camera and the second camera.

Abstract: A method of determining ripeness of a growing mushroom involves obtaining a sequence of images of a growing mushroom over a period of time and, from the sequence of images, measuring one or more features of the growing mushroom to obtain temporal measurements for the one or more features. The temporal measurements indicate rates at which the one or more features are changing. The temporal measurements are compared to pre-determined ripeness functions to determine the ripeness of the mushroom. The mushroom is picked if the comparison indicates that the mushroom is ready to be harvested.

Abstract: In various examples, surface profile estimation and bump detection may be performed based on a three-dimensional (3D) point cloud. The 3D point cloud may be filtered in view of a portion of an environment including drivable free-space, and within a threshold height to factor out other objects or obstacles other than a driving surface and protuberances thereon. The 3D point cloud may be analyzed—e.g., using a sliding window of bounding shapes along a longitudinal or other heading direction—to determine one-dimensional (1D) signal profiles corresponding to heights along the driving surface. The profile itself may be used by a vehicle—e.g., an autonomous or semi-autonomous vehicle—to help in navigating the environment, and/or the profile may be used to detect bumps, humps, and/or other protuberances along the driving surface, in addition to a location, orientation, and geometry thereof.

Abstract: A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

Abstract: A method, apparatus, system, and computer program product for monitoring a platform. Images are received by a computer system from a sensor system positioned to monitor movement of human beings relative to the platform. A set of the human beings is identified by the computer system in the images. The movement of the set of the human beings relative to the platform is determined by the computer system using the images. A count of the human beings on the platform is determined by the computer system based on the movement determined for the set of the human beings. A set of actions is performed by the computer system based on the count of the human beings on the platform.