A method for constructing a three-dimensional road network map is disclosed. The method includes: extracting a two-dimensional road based on a satellite remote sensing image; skeletonizing the two-dimensional road to obtain a two-dimensional road network map; and determining a height of each point in the two-dimensional road network map based on an elevation of three-dimensional point cloud data, to obtain the three-dimensional road network map.

A method for constructing a three-dimensional road network map is disclosed. The method includes: extracting a two-dimensional road based on a satellite remote sensing image; skeletonizing the two-dimensional road to obtain a two-dimensional road network map; and determining a height of each point in the two-dimensional road network map based on an elevation of three-dimensional point cloud data, to obtain the three-dimensional road network map.

A method and device for harmony-aware audio-driven motion synthesis are provided. The method includes determining a plurality of testing meter units according to an input audio, each testing meter unit corresponding to an input audio sequence of the input audio, obtaining an auditory input corresponding to each testing meter unit, obtaining an initial pose of each testing meter unit as a visual input based on a visual motion sequence synthesized for a previous testing meter unit, and automatically generating a harmony-aware motion sequence corresponding to the input audio using a generator of a generative adversarial network (GAN) model. The GAN model is trained by incorporating a hybrid loss function. The hybrid loss function includes a multi-space pose loss, a harmony loss, and a GAN loss. The harmony loss is determined according to beat consistencies of audio-visual beat pairs.

The present disclosure provides a method, apparatus, medium and device for extracting a river drying-up region and frequency, and belongs to the technical field of remote sensing. The method for extracting a river drying-up region and frequency can be applied to efficiently acquiring river drying-up information for a long time within the large spatial region. With the inverse normalized difference water index (iNDWI) and the maximum value composite (MVC) method for the remote sensing images, the present disclosure omits the troublesome step of separately extracting a river range for each image in the conventional method. In addition, the present disclosure quickly obtains the drying-up frequency by counting the total number of available images and the number of non-water images at the same pixel position, and yields a greater efficiency for extracting the river drying-up region and frequency.

The present disclosure provides a method, apparatus, medium and device for extracting a river drying-up region and frequency, and belongs to the technical field of remote sensing. The method for extracting a river drying-up region and frequency can be applied to efficiently acquiring river drying-up information for a long time within the large spatial region. With the inverse normalized difference water index (iNDWI) and the maximum value composite (MVC) method for the remote sensing images, the present disclosure omits the troublesome step of separately extracting a river range for each image in the conventional method. In addition, the present disclosure quickly obtains the drying-up frequency by counting the total number of available images and the number of non-water images at the same pixel position, and yields a greater efficiency for extracting the river drying-up region and frequency.

Disclosed is a non-transitory computer-readable recording medium recorded with a cardiopulmonary resuscitation training program executable by a processor of an information processing apparatus, the cardiopulmonary resuscitation training program causing the processor to perform operations including evaluating a posture of a person who is performing chest compressions, based on posture information indicating the posture obtained from a posture detection apparatus and ideal posture information indicating an ideal posture for the chest compressions stored in a storage unit, to yield an evaluation result, and displaying the evaluation result on a display apparatus.

A height estimation apparatus (10) according to the present disclosure includes an acquisition unit (11) for acquiring a two-dimensional image obtained by capturing an animal, a detection unit (12) for detecting a two-dimensional skeletal structure of the animal based on the two-dimensional image acquired by the acquisition unit (11), and an estimation unit (13) for estimating a height of the animal in a three-dimensional real world based on the two-dimensional skeletal structure detected by the detection unit (12) and an imaging parameter of the two-dimensional image acquired by the acquisition unit (11).

Implementations generally recognize clinical activity using multiple cameras. In some implementations, a method includes obtaining a plurality of videos of a plurality of objects in an environment. The method further includes determining one or more key points for each object of the plurality of objects. The method further includes recognizing activity information based on the one or more key points. The method further includes computing workflow information based on the activity information.

A system for training a neural network for action recognition based on unlabeled action sequences includes a first neural network (NN1) and a second neural network (NN2). A first updating module is arranged to update parameters of NN1 to minimize a difference between representation data generated by NN1 and representation data generated by NN2. A second updating module is arranged to update parameters of NN2 as a function of the parameters of NN1. An augmentation module includes first and second sub-modules and is configured to include augmented versions of incoming action sequences in first and second input data. The first and second sub-modules are configured to apply at least partly different augmentation to the incoming action sequences. After NN1 and NN2 have been operated on one or more instances of the first and second input data, NN1 comprises a parameter definition of a pre-trained neural network.

A system for training a neural network for action recognition based on unlabeled action sequences includes a first neural network (NN1) and a second neural network (NN2). A first updating module is arranged to update parameters of NN1 to minimize a difference between representation data generated by NN1 and representation data generated by NN2. A second updating module is arranged to update parameters of NN2 as a function of the parameters of NN1. An augmentation module includes first and second sub-modules and is configured to include augmented versions of incoming action sequences in first and second input data. The first and second sub-modules are configured to apply at least partly different augmentation to the incoming action sequences. After NN1 and NN2 have been operated on one or more instances of the first and second input data, NN1 comprises a parameter definition of a pre-trained neural network.