A control method for a mobile platform includes obtaining a captured image, identifying one or more candidate first characteristic parts from the captured image, determining a second characteristic part of a target object in the captured image, determining one or more matching parameters each corresponding to one of the one or more candidate first characteristic parts based on the one or more candidate first characteristic parts and the second characteristic part, determining a target first characteristic part of the target object from the one or more candidate first characteristic parts based on the one or more matching parameters, and switching from tracking the second characteristic part to tracking the target first characteristic part in response to a tracking parameter of the target object meeting a preset tracking condition.

Various techniques are provided for generating reports of three dimensional (3D) images. The techniques include identifying a plurality of volume features in a 3D image using a first machine learning (ML) module trained with annotated 3D images, and identifying a plurality of semantic representations associated with the 3D image using a second ML module trained with the annotated 3D images and reports associated with the annotated 3D images. The techniques further include generating a report of the 3D image based on the volume features and the semantic representations using a third ML module trained with the reports and outputs generated by the first ML module and the second ML module using the annotated 3D images and the reports.

The present invention discloses an object modeling and movement method. The method is applied to a mobile terminal, and the mobile terminal includes a color camera and a depth camera. The method includes: performing panoramic scanning on a target object by using the color camera and the depth camera, to obtain a 3D model of the target object; obtaining a target skeletal model; fusing the target skeletal model and the 3D model of the target object; obtaining a target movement manner; and controlling the target skeletal model in the target movement manner, to animate the 3D model of the target object in the target movement manner. This can implement integration from scanning, 3D reconstruction, skeletal rigging, to preset animation display for an object on one terminal, thereby implementing dynamization of a static object, and increasing interest in using the mobile terminal by a user.

An advanced driver assistance system (ADAS) of a vehicle and associated method is disclosed. A first set of sensed lane measurements from a first imaging device and a second set of sensed lane measurements from a second imaging device are obtained. Each of the first and second sets of sensed lane measurements includes a lane estimate for the lane lines on a roadway. Each lane estimate is associated with one lane line. For each lane line, the associated lane estimates from the first and second sets of sensed lane measurements are fused to obtain a fused lane estimate, from which a representative model lane estimate is determined. For each of the plurality of lane lines, the associated lane estimates from the first and second sets of sensed lane measurements and the representative model lane estimate are fused to obtain a corrected fused lane estimate, which is output.

Embodiments of the present disclosure may provide methods and systems enabled to perform the following stages: receiving a plurality of content streams; retrieving metadata associated with each of the plurality of content streams; processing the metadata to detect at least one target annotation within at least one target content stream; retrieving telemetry data associated with the at least one target content stream; processing the telemetry data and the metadata associated with a plurality of frames in the at least one target content stream to ascertain vector motion data; and mapping a spatial relationship associated with at least one capturing device associated with at least one target content source.

Systems and methods of capturing images are disclosed. For instance, a plurality of position signals associated with a mobile computing device can be received, the plurality of position signals can be obtained at least in part using one or more sensors implemented within the mobile computing device. A relative motion between the mobile computing device and a scattering point associated with a target can be determined. A plurality of return signals reflected from the scattering point can be received. Each return signal can correspond to a pulse transmitted by the mobile computing device. A target response associated with the scattering point can be determined based at least in part on the relative motion between the mobile computing device and the scattering point.

An object management device manages an object based on a microscopic pattern on the surface of the object included in an image of the surface of the object. The object management device has a position correction unit. The position correction unit aligns the image based on the microscopic pattern appearing in common on a plurality of objects.

A learning method for supporting a safer autonomous driving through a fusion of information acquired from images and communications is provided. And the method includes steps of: (a) a learning device instructing a first neural network and a second neural network to generate an image-based feature map and a communication-based feature map by using a circumstance image and circumstance communication information; (b) the learning device instructing a third neural network to apply a third neural network operation to the image-based feature map and the communication-based feature map to generate an integrated feature map; (c) the learning device instructing a fourth neural network to apply a fourth neural network operation to the integrated feature map to generate estimated surrounding motion information; and (d) the learning device instructing a first loss layer to train parameters of the first to the fourth neural networks.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

An online system is configured to provide content items to users. The content item includes an image, and is displayed with an interface element colored using an accent color to create a unified look and feel with the displayed image. The accent color is dynamically selected based upon the image, extracted color features of the image, and embeddings associated with the image indicating at least one object depicted in the image. A machine-trained classification model selects the color to be used in displaying the interface element from a quantized set of colors of the image, based upon the extracted color features and the embeddings associated with the image. As such, suitable accent colors can be selected automatically for large numbers of content items, in a flexible manner that can account for the context of the images and the context in which the content item is to be displayed.