A method for detecting unmanned aerial vehicles (UAV) includes detecting an unknown flying object in a monitored zone of air space. An image of the detected unknown flying object is captured. The captured image is analyzed to classify the detected unknown flying object. A determination is made, based on the analyzed image, whether the detected unknown flying object comprises a UAV.

A method for detecting unmanned aerial vehicles (UAV) includes detecting an unknown flying object in a monitored zone of air space. An image of the detected unknown flying object is captured. The captured image is analyzed to classify the detected unknown flying object. A determination is made, based on the analyzed image, whether the detected unknown flying object comprises a UAV.

A notched 2D shape may encode information. For instance, a physical tag may display, form or include a polygon that is modified by notches and by one or more holes. This notched 2D shape may encode data that identifies, or provides information regarding, a physical product to which the tag is physically attached. Alternatively, this notched 2D shape may encode any other type of information, such as information about what we sometimes call a product shape or shape matrix. The notched shape may be an octagon that is modified by notches and by one or more holes.

Aspects of the present disclosure relate to systems and methods for active depth sensing. An example apparatus configured to perform active depth sensing includes a projector. The projector is configured to emit a first distribution of light during a first time and emit a second distribution of light different from the first distribution of light during a second time. A set of final depth values of one or more objects in a scene is based on one or more reflections of the first distribution of light and one or more reflections of the second distribution of light. The projector may include a laser array, and the apparatus may be configured to switch between a first plurality of lasers of the laser array to emit light during the first time and a second plurality of laser to emit light during the second time.

Aspects of the present disclosure relate to systems and methods for active depth sensing. An example apparatus configured to perform active depth sensing includes a projector. The projector is configured to emit a first distribution of light during a first time and emit a second distribution of light different from the first distribution of light during a second time. A set of final depth values of one or more objects in a scene is based on one or more reflections of the first distribution of light and one or more reflections of the second distribution of light. The projector may include a laser array, and the apparatus may be configured to switch between a first plurality of lasers of the laser array to emit light during the first time and a second plurality of laser to emit light during the second time.

A method and apparatus for processing human body model data, an electronic device and a storage medium are provided. The method includes: obtaining 3D human body model data, and classifying the 3D human body model data into multiple data sets according to a predetermined classification condition, wherein the predetermined classification condition includes medical anatomy category information and art resource category information; determining, according to each of the data sets, a duplicate resource in the data set, and reorganized data sets where the duplicate resource is removed; and packing each of the duplicate resource and the reorganized data sets into a respective data package, and storing all of the data packages.

A method and apparatus for processing human body model data, an electronic device and a storage medium are provided. The method includes: obtaining 3D human body model data, and classifying the 3D human body model data into multiple data sets according to a predetermined classification condition, wherein the predetermined classification condition includes medical anatomy category information and art resource category information; determining, according to each of the data sets, a duplicate resource in the data set, and reorganized data sets where the duplicate resource is removed; and packing each of the duplicate resource and the reorganized data sets into a respective data package, and storing all of the data packages.

A surveillance sensor system for a surveillance network configured to monitor an environment surrounding a device, and including a processing unit, a tridimensional sensor and a camera. The surveillance sensor system is providing to the surveillance network, a global tridimensional map, a plurality of features including associations of the features to the corresponding tridimensional data points in the global tridimensional map, and including properties determined for each feature. The surveillance sensor system is not providing the images from the camera to the surveillance network.

A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.