A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: performing first target feature extraction on image features of an image to obtain first target features; performing first interaction feature extraction on image features to obtain first interaction features and scores thereof; determining at least some first interaction features in the first interaction features based on the score of each of the first interaction features; determining first motion features based on the at least some first interaction features and the image features; processing the first target features to obtain target information of targets in the image; processing the first motion features to obtain motion information of one or more motions in the image; and matching the targets with the motions to obtain a human-object interaction detection result.

Systems and methods for identifying inclined regions are provided. In one aspect, a method is provided that includes receiving shadow data for at least one first ground object in a first region, wherein each first ground object is depicted in one overhead image of the first region, wherein the shadow data comprises a length of the respective first ground object as identified from the respective overhead image; receiving shadow data for at least one second comparable ground object in a second region, wherein each second ground object is depicted in one overhead image of the second region, wherein the shadow data comprises a length of the respective second ground object as identified from the respective overhead image; calculating a statistical measure describing the variability of the shadow lengths between objects in the first region and the second region; comparing the statistical measure to a predetermined threshold; and based on the comparison, identifying that the first region is inclined relative to the second region.

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.

There is provided an image processing system and method for identifying a user. The system comprises a processor configured to identify a first user in an image, determine a plurality of characteristic vectors associated with the first user, compare the characteristic vectors associated with the first user with a plurality of predetermined characteristic vectors associated with a plurality of users including the first user, and identify the first user based on the comparison.

A target detection method and apparatus. The method comprises: acquiring an input image, and sending same to a candidate region generation network to generate a plurality of regions of interest; formatting the plurality of regions of interest, and then sending same to a target key point network to generate a thermodynamic diagram; using a global feature map of the input image to perform convolution on the thermodynamic diagram, so as to generate a local depth feature map; and fusing the global feature map and the local depth feature map, and detecting a target therefrom by means of a detector. The present invention can be applied to target detection at different scales, improves the detection accuracy and robustness of a target detection technique for an occluded target in complex scenarios, and achieves, by means of making full use of local key point information of the target, target positioning under occlusion.

A computer-executable method relating to weeds, and a computer system. The method comprises: receiving an image (S11); recognizing one or more plants in the image in order to obtain the classification and/or names of the plants, and determining whether the plants are weeds (S12); and in response to determining that at least one plant is a weed, outputting information indicating that the at least one plant is a weed (S13).

The present application provides an action recognition method and apparatus and an electronic device. The method includes: if a target object is detected from a video frame, acquiring a plurality of images containing the target object, and optical-flow images of the plurality of images; extracting an object trajectory feature of the target object from the plurality of images, and extracting an optical-flow trajectory feature of the target object from the optical-flow images of the plurality of images; and according to the object trajectory feature and the optical-flow trajectory feature, recognizing a type of an action of the target object. Because it combines the time-feature information and the spatial-feature information of the target object, effectively increases the accuracy of the detection and recognition on the action type, and may take into consideration the detection efficiency at the same time, thereby improving the overall detection performance.

Systems and methods include an image capture component configured to capture infrared images of a scene, and a logic device configured to identify a target in the images, acquire temperature data associated with the target based on the images, evaluate the temperature data and determine a corresponding temperature classification, and process the identified target in accordance with the temperature classification. The logic device identifies a person and tracks the person across a subset of the images, identify a measurement location for the target in a subset of the images based on target feature points identified by a neural network, and measure a temperature of the location using corresponding values from one or more captured thermal images. The logic device is further configured calculate a core body temperature of the target using the temperature data to determine whether the target has a fever and calibrate using one or more black bodies.

A processing load in a case where a plurality of different sensors is used can be reduced. An information processing apparatus according to an embodiment includes: a recognition processing unit (15, 40b) configured to perform recognition processing for recognizing a target object by adding, to an output of a first sensor (23), region information that is generated according to object likelihood detected in a process of object recognition processing based on an output of a second sensor (21) different from the first sensor.

A receiver receives a radio wave transmitted to a target and scattered by the target to acquire a signal. An imaging unit generates a 3D complex image of the target based on the signal. A value extraction unit extracts intensity information and phase in including an intensity matrix and a phase matrix, the extracted intensity information constituting the intensity matrix and the extracted phases information constituting the phase matrix. A subset selection unit selects a subset from the value set. A transformation unit changes a representation of the subset to generate a 2D real image. A detection unit detects whether there is an undesired object on the target based on the 2D real image.