2491 results about "Mobile object" patented technology

In some embodiments, a computer system facilitates the movement and / or placement of first virtual objects with respect to second virtual objects in a three-dimensional environment in accordance with some embodiments. In some embodiments, the computer system utilizes different movement algorithms for moving objects in a three-dimensional environment in accordance with some embodiments.

The invention relates to the technical field of synchronous acquisition, in particular to a multi-camera synchronous data acquisition method and system, and the method comprises the following steps: based on a real-time video stream of a camera, analyzing a video frame, recognizing a moving object in a differentiated video stream, and carrying out the recognition of the moving object in the differentiated video stream; and synchronously recording the occurrence time and position change of the moving object in the differentiated camera to obtain view angle change data. According to the invention, the angle and focal length of the camera are synchronously adjusted to cope with the view angle change of different scenes, and the trigger time of the camera is uniformly configured and is synchronously started through the network, so that the time deviation is reduced, the analysis of the synchronous trigger data set and the timestamp adjustment are synchronously triggered, and the synchronization between the cameras is optimized; the consistency and synchronism of the video streams obtained from different angles are ensured, the data transmission parameters are dynamically adjusted according to the network condition, the efficiency and synchronization precision in the data transmission process are improved, and the influence of network delay and bandwidth change is effectively dealt with.

In some embodiments, a computer system performs different object selection-related operations. In some embodiments, a computer system places objects at locations in a displayed region based on attention of a user. In some embodiments, a computer system displays a container virtual object with curvature in a three-dimensional environment.

An information provision device, information provision method, and a non-transitory recording medium storing a program for causing a computer to execute the information provision method. Each of the information provision device and the information provision method includes projecting an image light to a light transmission member to display a for-driver information image indicating for-driver information to be provided to a driver of a mobile object in a prescribed display area, and displaying at a first point where a normal for-driver information image is displayed a priority for-driver information image in place of the normal for-driver information image, where the displaying includes displaying the normal for-driver information image at a second point different from the first point for at least some of a period during which the priority for-driver information image is displayed at the first point.

An image enhancement and object detection system is provided to enhance the degraded images and bring out the inherent details of the image received via an underwater surveillance system. The said system includes a novel deep learning architecture i.e., a Zero-DCE-U that enhances the degraded underwater images using modified UIQM. Further, a new training method using a single image based on correlation is employed. Furthermore, a G-UNet architecture is configured to detect moving objects while preserving the spatial-contextual relationships.

A mounting system capable of mounting objects to support structures is disclosed. The mounting system includes a wall mount including a display bracket configured to hold the object, a fixed support bracket couplable to a vertical support structure, and a linkage assembly. The linkage assembly has a low-profile stowed configuration in which the object is held in a raised position close to the support structure. The linkage assembly moves to another configuration to move the object. The object can be held in a lowered position. A pinion adjustment mechanism can be used to control a biasing mechanism for facilitate convenient movement of the object.

The present invention relates to a trajectory generating method for moving a Robot Arm in the fastest and smoothest path by avoiding obstructions and jerky movements in the shortest possible time to predetermined endpoints, by implementing a planning, sequence and trajectory path manager to plan the movements ahead of time and concurrently creating a database of trajectory paths that is subsequently used for storing and retrieving of repetitive movements of the Robot arm, resulting in time saving and smooth movements. In a dynamically changing operating environment, endpoints are calculated through the implementation of an intelligence module that captures a three-dimensional image of the processing area and predicts a 3D pose, which in turn is translated to positional coordinates for the Robot to utilise, to move to the target endpoint. The planning manager working in sync with the intelligence module and sequence manager enables a very favourable environment to seamlessly and efficiently move objects smoothly at controlled speeds, mimicking human movement.

A method for position and / or deviation correction of a moving object in a simultaneous localization and mapping (SLAM) environment. The method comprises receiving at the moving object point cloud data for an area associated with the moving object from a sensor located at a fixed location. A processor of the moving object performs the steps of: transforming the received point cloud data to a coordinate system of the moving object; matching the transformed point cloud data with point cloud data generated by a sensor at the moving object for the area associated with the moving object; and using the matched point cloud data to determine a corrected position and / or deviation of the moving object within a selected coordinate system.

Techniques for detecting and determining a density for a non-impeding object based on correlation between radar and lidar returns are described herein. The techniques provide for receiving lidar data and radar data from a vehicle system operating in an environment. Portions of the lidar data and radar data are determined based on being associated with moving objects in the environment. The portions are then correlated to determine a similarity between the radar and lidar data. The similarity may then be used to determine an indication and density of the non-impeding object in the environment and cause the vehicle to operate within the environment accordingly.

Provided is a small, simple hydrogen gas production device that allows gas produced in sequence using liquefied ammonia as a raw material to flow downstream without stagnation, does not require a separate pressure reducer for creating a pressure difference between the upstream and downstream sides of a separation membrane, and can remove nitrogen without using a high-pressure compressor, thereby consuming less power. [Solution] The hydrogen gas production device of the embodiment includes a tank of liquefied ammonia, a primary purification section and a secondary purification section that remove residual ammonia gas and nitrogen gas from decomposition gas of ammonia gas, and a pressure reducer. The separation membrane of the purification section from which nitrogen gas is removed separates hydrogen gas from nitrogen gas by the differential pressure between the upstream and downstream sides of the separation membrane. The pressure reducer is provided in a purified gas line through which hydrogen gas flows, and reduces the pressure on the upstream side of the pressure reducer. The differential pressure is formed as a part of the pressure difference formed between the tank internal pressure and the pressure on the upstream side of the pressure reducer.

The invention discloses a video analysis technology-based monitoring terminal for a child to take an elevator alone, relates to the technical field of elevator monitoring, and solves the problem that whether the child takes the elevator alone or not cannot be effectively identified. According to the method, the moving object can be accurately determined from the video of the elevator door, the moving object can be accurately recognized through calculation and comparison of pixel point features, gradient values and the like and determination of the same point location and the moving point location, the object recognition accuracy in a complex video scene is improved, and the user experience is improved. According to the method, the moving object and the related human body template are combined and compared to recognize the character objects and determine the specific number of the character objects entering the elevator, the height of the character objects is determined through the height determining end based on feature point mapping and distance calculation, whether the person entering the elevator is a child or not can be accurately judged, and the safety of the elevator is improved. And a reliable basis is provided for subsequent early warning.

The invention discloses a multi-scene internet of things off-site dynamic inspection self-adaptive monitoring system, and relates to the technical field of intelligent inspection and path planning, the system is composed of a plurality of functional modules, and the system comprises a moving object detection module for detecting a moving object by adopting a Gaussian mixture background modeling and optical flow method; meanwhile, a light-weight MobileViT-XXS model is deployed, and a moving object target is tracked; the moving object classification and identification module is used for judging the type of a moving object through a multi-modal classification decision tree: when the moving object is judged to be a non-living body, carrying out ROI extraction and optimal frame interception on the image saliency of the moving object, and generating EXIF metadata through super-resolution reconstruction; when the judgment result is a biological object, biological category judgment is carried out: when the judgment result is an animal, geo-fencing data matching is carried out, and a spatial-temporal feature fusion model is adopted; and when human beings are judged, face quality evaluation is carried out through multi-frame fusion super-resolution.

A detection device that detects a moving object included in a captured image includes: an image acquisition unit that acquires the captured image; a type acquisition unit that acquires type information indicating one body type of the moving object included in the captured image, the one body type being classified according to an exterior shape of the moving object; a model acquisition unit that acquires, from among a plurality of first detection models each of which is a machine learning model prepared for each of the one body type, the first detection model selected according to the one body type identified by the type information acquired by the type acquisition unit; and a detection unit that detects the moving object included in the captured image by inputting the captured image to the first detection model acquired by the model acquisition unit and identifying a target region indicating the moving object in the captured image.

A method and system for controlled absence of moving objects from the field of view of a camera during a defined time period is disclosed. When data generated by an at least one electronics device or a respective controller thereof confirms that the physical environment requirements of the at least one electronics device will be met, or confirms that the at least one electronics device will be controlled to meet the operational requirements, the video camera is allowed to enter a mode where the capturing of the video is impeded during the time period. Conversely, when the data indicates that the physical environment requirements will not be met, or that the at least one electronics device will not be controlled to meet the operational requirements, the system ensures that the capturing of the video is not impeded during the time period.

A unified mapping apparatus for path planning and global localization for a mobility device such as an autonomous or self-guiding robot or drone is provided. The unified mapping apparatus comprises a static mapping module and a dynamic mapping module. The primary function of the static mapping module is to scan the mobility device's surrounding environment static geometric for the eventual goal of global path planning. The static objects scanned and identified by the static mapping module are used for global localization. The primary function of the dynamic mapping module is to track moving objects in mobility device's surrounding environment, and predict such objects' movements. With the prediction of dynamic objects' movements, path planning can be improved substantially.

The invention discloses a motion vector refinement for oversampling of time domain spread. A residual network is used to predict a set of residual motion vectors that provides additional motion data for portions of frames for which motion vectors are not provided, such as animation textures, mirrored / reflective objects, and / or moving objects without motion information.

A processing system that executes a process for performing presentation to a driver of a moving object is provided. The processing system includes at least one processor. The processor executes evaluating driving of the driver using a rule defined by a safety model of autonomous driving, and outputting information related to teaching for complying with the rule in a presentable manner to the driver based on the evaluating.

This display control device includes: a first mobile object information acquiring unit that acquires first mobile object information indicating the position, moving speed, and direction of movement of a first mobile object moving in a facility; a second mobile object information acquiring unit that acquires second mobile object information indicating the position, moving speed, and direction of movement of a second mobile object moving in the facility; an image acquisition unit that acquires, on the basis of the first mobile object information acquired by the first mobile object information acquiring unit and the second mobile object information acquired by the second mobile object information acquiring unit, image information indicating a display image to be displayed in a space in the facility by a display output device installed in the facility; and an image output unit that outputs the image information acquired by the image acquisition unit.

A monocular image depth estimation method and apparatus, and a computer device are provided. The method includes: obtaining a first depth map of a to-be-estimated image; obtaining a dynamic point set and a first pose transformation result of a to-be-estimated millimeter-wave point cloud; obtaining a second depth map of a latter frame of the to-be-estimated image; calculating a projection error between a first depth map and the second depth map of the latter frame of the to-be-estimated image; obtaining a second pose transformation result of the to-be-estimated millimeter-wave point cloud; obtaining a pose estimation error; calculating a depth error of a moving object in two frames of the to-be-estimated image; and obtaining an overall training loss, obtaining a complete depth estimation model, and performing monocular image depth estimation on the to-be-estimated image.

A method for training a model for discovering objects in an input image sequence, the model includes an encoder; an attention module configured to transform the first feature vector into a plurality of feature vectors, called slots; a decoder; the learning of the attention maps being monitored by a set of binary masks for discovering mobile objects produced by an external source, called pseudo-labels; the pseudo-labels being filtered by means of the following steps of: determining an attention map of the foreground of the image; computing a confidence score from the average of the values of the attention map of the foreground of the image at the positions of each mobile object present in a pseudo-label; filtering the mobile objects of the pseudo-labels for which the confidence score is below a predefined threshold.

The invention relates to the technical field of automatic driving, and provides an intersection steering decision-making method and decision-making device. According to the method, target obstacle information of a moving object is obtained, the conflict time of the moving object and a target obstacle is determined, and a revenue function is constructed based on the conflict time, the self-vehicle decision probability of the moving object to the target obstacle and the decision probability of the target obstacle. Solving the revenue function to obtain a first target self-vehicle decision probability when the revenue function reaches a balance point, determining a convergence decision by using the first target self-vehicle decision probability, determining that the decision type of the convergence decision is different from the current decision type of the moving object, performing feasibility judgment and enhancement judgment on decision type conversion operation, according to the method, the intersection turning decision is made by combining the game and the rule, the more accurate decision result can be provided by dynamically adapting to the environment change through the game, the calculated amount is simplified by utilizing the constraint of the rule, the decision efficiency is greatly improved on the basis of not obviously reducing the decision quality, and the decision-making efficiency is improved. And thus, the road condition steering decision making efficiency is improved.

Provided is a method for predicting a location of a fast-moving object. The method includes receiving event information from an event camera, the event information corresponding to an event detected by the event camera, generating a Binary Event History Image (BEHI) based on the event information, providing the BEHI as an input to an event-based neural network, obtaining, as an output of the event-based neural network, a first predicted location of the fast-moving object, a normal distribution indicating prediction uncertainty of the predicted location, and a predicted time-to-collision (TTC). The method further includes estimating a second predicted location of the fast-moving object based on the first predicted location, the normal distribution, and the predicted TTC output by the event-based neural network, and actuating a mechanical catching device to be at the second predicted location.

Disclosed herein are methods, devices, modules, and systems that can be used to accurately track and subsequently target objects of interest relative to a moving body, such as a vehicle. The object tracking methods can be implemented by a detection system in which a sensor is coupled to the moving body. The targeting system can be used to target objects tracked by the detection system, such as for automating crop cultivation or maintenance. The devices disclosed herein can be configured to locate, identify, and autonomously target weeds with a beam, such as a laser beam, that can burn or irradiate the weeds. These methods, devices, modules, and systems can be used for agricultural crop management or home weed control.

To locate and model a moving 3D object captured in a time sequence of multiple images, semantic keypoint detection is applied to each image, in order to generate a set of 2D semantic keypoint detections in an image plane of the image. For each image, a camera pose and an initial object pose is received. Based on the initial 3D model, and the initial object pose and the camera pose for each image, a set of keypoint projections is computed in the image plane of the image, the keypoint projections being 2D projections of 3D semantic keypoints of the initial 3D model. A new 3D model and a new object pose are determined for each image based on an aggregate reprojection error between the 2D semantic keypoint detections and the keypoint projections across the time sequence of multiple images.

An information processing device includes a location information obtaining unit that, from each of a plurality of imaging devices which is used in a mobile object and which searches for an object, obtains location information indicating the current location of the imaging device; and a deciding unit that, based on the location information of each of the plurality of imaging devices, decides on the imaging device to be used in searching for the object.

To provide a positioning device capable of improving positioning accuracy, a positioning method, a program, and a storage device.SOLUTION: A positioning device 20 includes a first position estimation unit 31, a second position estimation unit 33, and an information integration unit 36. The first position estimation unit 31 and the second position estimation unit 33 output first information and second information by positioning in different coordinate systems (a world geodetic system and a map coordinate system), respectively. The information integration unit 36 performs predetermined optimization processing relative to a combination of the first information and the second information recognized as the same time. The information integration unit 36 acquires difference (inter-coordinate difference) regarding information such as a position and a posture of a moving object between mutually different coordinate systems of the first information and the second information by predetermined optimization processing.SELECTED DRAWING: Figure 1

The invention relates to the technical field of attitude control, in particular to a moving object attitude control method based on an improved LKF. Predicting prior attitude estimation at the next moment; calculating a process error covariance matrix; calculating a measurement attitude; calculating a quaternion measurement error covariance matrix; constructing feature vectors of acceleration interference and geomagnetic interference amplitude levels, and calculating an interference amplitude level value by using a Gaussian function model; updating the process error covariance matrix and the measurement error covariance matrix by using the interference amplitude level value; calculating a priori attitude estimation error covariance matrix by using the updated process error covariance matrix; calculating a Kalman gain matrix by using the priori attitude estimation error covariance matrix and the updated measurement error covariance matrix; calculating attitude posteriori estimation at the next moment; and performing attitude estimation at the next moment. According to the method, the problems of insufficient estimation precision and poor interference suppression capability of the attitude observation error covariance matrix of the existing LKF are solved.