Disclosed are a method and system for testing a wearable device. The method includes: performing an angle acquisition process for at least two times, and calculating an optical imaging parameter value of a target virtual image on the basis of angle variation values acquired in the at least two angle acquisition processes. With the method and system according to the present disclosure, the finally calculated optical imaging parameter value is more objective and more accurate than that acquired by means of the human eyes.

Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

A method for re-recognizing an object image is provided based on multi-feature information capture and correlation analysis weights of an input feature map by using a convolutional layer with a spatial attention mechanism and a channel attention mechanism, causing channel and spatial information to effectively combined, which not only focus on an important feature and suppress an unnecessary feature, but also improve a representation of a feature. A multi-head attention mechanism is used to process a feature after an image is divided into blocks to capture abundant feature information and determine a correlation between features to improve performance and efficiency of object image retrieval. The convolutional layer with the channel attention mechanism and the spatial attention mechanism is combined with a transformer having the multi-head attention mechanism to focus on globally important features and capture fine-grained features, thereby improving performance of re-recognition.

Systems, devices, and methods for training a neural network and performing image interest point detection and description using the neural network. The neural network may include an interest point detector subnetwork and a descriptor subnetwork. An optical device may include at least one camera for capturing a first image and a second image. A first set of interest points and a first descriptor may be calculated using the neural network based on the first image, and a second set of interest points and a second descriptor may be calculated using the neural network based on the second image. A homography between the first image and the second image may be determined based on the first and second sets of interest points and the first and second descriptors. The optical device may adjust virtual image light being projected onto an eyepiece based on the homography.

The present invention makes it possible to cause an action recognizer capable of recognizing actions with high accuracy and with a small quantity of learning data to learn. An input unit 101 receives input of a learning video and an action label indicating an action of an object, a detection unit 102 detects a plurality of objects included in each frame image included in the learning video, a direction calculation unit 103 calculates a direction of a reference object, which is an object to be used as a reference among the plurality of detected objects, a normalization unit 104 normalizes the learning video so that a positional relationship between the reference object and another object becomes a predetermined relationship, and an optimization unit 106 optimizes parameters of an action recognizer to estimate the action of the object in the inputted video based on the action estimated by inputting the normalized learning video to the action recognizer and the action indicated by the action label.

An information display method, a device and a storage medium. The method includes: acquiring a first image including a first object in a video, determining whether a second object is present in the first image, and when it is determined that the second object is present in the first image and that the second object satisfies a preset positional relationship with the first object, superimposing a first material on an area where the second object is located in the first image. Using the above method, it is realized that when the second object is detected in the image, any material is superimposed on the area where the second object is located, so as to avoid the problem of not being able to use part of special effects or express information when the second object satisfies the preset positional relationship with the first object.

A detection system (10) includes: an acquisition unit (110) configured to acquire an image including a living body; and a detection unit (120) configured to detect, from the image, a feature figure corresponding to an appropriately circular first part on the living body, and feature points corresponding to a second part around the first part on the living body. According to such a detection system, the first part and the second part with different features in shape can be individually detected appropriately.

An auto-rotation module having a single-layer neural network on a user device can convert a document image to a monochrome image having black and white pixels and segment the monochrome image into bounding boxes, each bounding box defining a connected segment of black pixels in the monochrome image. The auto-rotation module can determine textual snippets from the bounding boxes and prepare them into input images for the single-layer neural network. The single-layer neural network is trained to process each input image, recognize a correct orientation, and output a set of results for each input image. Each result indicates a probability associated with a particular orientation. The auto-rotation module can examine the results, determine what degree of rotation is needed to achieve a correct orientation of the document image, and automatically rotate the document image by the degree of rotation needed to achieve the correct orientation of the document image.

A method for object monitoring of a rail vehicle using a video monitoring system includes capturing a measurement signal of a video camera and determining image data of an environment of a vehicle interior according to the captured measurement signal. A piece of luggage in the vehicle interior is identified according to the determined image data. The identified piece of luggage is personalized. A manipulation of the identified piece of luggage is determined according to a predefined manipulation condition. A notification signal is sent according to the determined manipulation and according to the personalizing of the identified piece of luggage. A device for operating a video monitoring system for a rail vehicle for object monitoring and a rail vehicle are also provided.

A method and apparatus for training a model, a method and apparatus for processing a video, a device and a storage medium are provided. An implementation of the method for training a model includes: analyzing a sample video, to determine a plurality of human body image frames in the sample video; determining human body-related parameters and camera-related parameters corresponding to each human body image frame; determining, based on the human body-related parameters, the camera-related parameters and an initial model, predicted image parameters of an image plane corresponding to the each human body image frame, the camera-related parameters and image parameters; and training the initial model based on original image parameters of the human body image frames in the sample video and the predicted image parameters of image planes corresponding to the human body image frames, to obtain a target model.