A vehicular trailering assist system includes a camera disposed at a rear portion of a vehicle. With a trailer hitched to the vehicle at a hitch ball of the vehicle, the camera views at least a portion of the trailer. An electronic control unit includes an image processor operable to process frames of image data captured by the camera. With the trailer hitched to the vehicle at the hitch ball of the vehicle, the vehicular trailering assist system determines trailer feature points via image processing of image data captured by the camera, and determines respective perpendicular bisectors of respective line segments extending between the determined trailer feature points in respective frames of captured image data while the vehicle tows the trailer and turns. The vehicular trailering assist system determines a location of the hitch ball based at least in part on an intersection point of the determined perpendicular bisectors.

There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

A visual positioning system, comprising a first visual sensor (501), a second visual sensor (502), and a position obtaining unit (503), wherein the first visual sensor (501) is used for obtaining a first image (G11) of a first position (A) of a target apparatus (7), a second visual sensor (502) is used for obtaining a second image of a second position (B) of the target apparatus (7), and the position obtaining unit (503) is used for obtaining position information of the target apparatus (7) according to the first image (G11) and the second image. Further disclosed are a battery swapping device and a battery swapping control method. Relatively high positioning accuracy is obtained in a visual manner, and accurate positioning between the battery swapping device and a vehicle for battery swap is implemented.

A system and a method for tracing components of an electronic assembly. The method may include obtaining an image of a component of the electronic assembly, generating identification information of the component based on visual features of the component, authenticating the component based on the identification information, generating correlating information by associating the component to an electronic assembly and classifying the component to a group based on the visual features of the component.

A method and a system for intelligently controlling children's usage of a screen terminal are provided in this disclosure, which relate to the technical field of intelligent home terminals. When children use the screen terminal, children's age can be automatically and intelligently identified, real-time intelligent supervision on children's sitting posture, distance and other aspects can be conducted according to different children's ages, so as to intelligently control on and off duration of the screen terminal, so as to guide the children to use the screen terminal device healthily. Compared with prior art schemes, in the disclosure, management of the screen terminal device can be realized without manual operation, reducing trouble from manual equipment management, further realizing specific control of children's usage of the screen terminal by age, increasing intelligence degree, and with advantage of being used in multiple scenes.

Provided are a reinforcement learning-based label-free six-dimensional object pose prediction method and apparatus. The method includes: obtaining a target image to be predicted, the target image being a two-dimensional image including a target object; performing pose prediction based on the target image by using a pre-trained pose prediction model to obtain a prediction result, the pose prediction model being obtained by performing reinforcement learning based on a sample image; and determining a three-dimensional position and a three-dimensional direction of the target object based on the prediction result. The pose prediction model is trained by introducing reinforcement learning, the pose prediction is performed based on the target image by using the pre-trained pose prediction model, and thus the problem of six-dimensional object pose estimation based on two-dimensional images can be solved in the absence of real pose annotation, which ensures the prediction effect of label-free six-dimensional object pose prediction.

Provided are an illegal building identification method and apparatus, a device, and a storage medium, which relate to the field of cloud computing. The specific implementation scheme is: acquiring a target image and a reference image associated with the target image; extracting a target building feature of the target image and a reference building feature of the reference image, respectively; and determining, according to the target building feature and the reference building feature, an illegal building identification result of the target image.

A display control apparatus (1) includes: an acquisition unit (6e) configured to acquire an invisible light image showing invisible light in a real space from a sensor; a map generation unit (6a) configured to generate an environment map indicating a shape of the real space based on the invisible light image; and a display control unit (6d) configured to control a display unit (4) such that a virtual object regarding a state of an invisible substance in the real space is superimposed on the real space based on the invisible light image.

A method for ascertaining a suitable deployment of a mobile measuring device within measurement surroundings, wherein first and second measurement surroundings containing first and second object features are automatically optically captured at the first deployment and tracked using a visual inertial system (VIS) and within the scope of changing the deployment. The first and second measurement surroundings are compared, wherein the comparison is based on searching for corresponding first and second object features visible in a certain number and quality in the first and second measurement surroundings, wherein this certain number and quality of corresponding features is a criterion that a registration of the first and second point cloud is possible.

A computer assisted system is disclosed that includes an optical tracking system and one or more computing devices. The optical tracking system includes an RGB sensor and is configured to capture color images of an environment in the visible light spectrum and tracking images of fiducials in the environment in a near-infrared spectrum. The computer assisted system is configured to generate a color image of the environment using the color images, identify fiducial locations using the tracking images, generate depth maps from the color images, reconstruct three-dimensional surfaces of structures based on the depth maps, and output a display comprising the reconstructed three-dimensional surface and one or more surgical objects that are associated with the tracked fiducials. The computer assisted system can further include a monitor or a head-mounted display (HMD) configured to present augmented reality (AR) images during a procedure.