A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: extracting a plurality of first target features and one or more first motion features from an image feature of an image to be detected; fusing each first target feature and some of the first motion features to obtain enhanced first target features; fusing each first motion feature and some of the first target features to obtain enhanced first motion features; processing the enhanced first target features to obtain target information of a plurality of targets including human targets and object targets; processing the enhanced first motion features to obtain motion information of one or more motions, where each motion is associated with one human target and one object target; and matching the plurality of targets with the one or more motions to obtain a human-object interaction detection result.

A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: extracting a plurality of first target features and one or more first motion features from an image feature of an image to be detected; fusing each first target feature and some of the first motion features to obtain enhanced first target features; fusing each first motion feature and some of the first target features to obtain enhanced first motion features; processing the enhanced first target features to obtain target information of a plurality of targets including human targets and object targets; processing the enhanced first motion features to obtain motion information of one or more motions, where each motion is associated with one human target and one object target; and matching the plurality of targets with the one or more motions to obtain a human-object interaction detection result.

There is provided a computer implemented method of extracting information about a person. Incoming sensor signals for monitoring people within a field of view of a sensor system are received and processed. In response to detecting a person located within a notification region, an output device outputs a notification to the detected person. Processing of the incoming sensor signals continues in order to monitor behaviour patterns of the person and determine from his behaviour patterns whether he is currently in a consenting or non-consenting state. An extraction function attempts to extract information about the person irrespective of his determined state. A sharing function determines whether or not to share an extracted piece of information about the person with a receiving entity in accordance with his determined state, the information not being shared unless and until it is subsequently determined that the person is in the consenting state.

There is provided a computer implemented method of extracting information about a person. Incoming sensor signals for monitoring people within a field of view of a sensor system are received and processed. In response to detecting a person located within a notification region, an output device outputs a notification to the detected person. Processing of the incoming sensor signals continues in order to monitor behaviour patterns of the person and determine from his behaviour patterns whether he is currently in a consenting or non-consenting state. An extraction function attempts to extract information about the person irrespective of his determined state. A sharing function determines whether or not to share an extracted piece of information about the person with a receiving entity in accordance with his determined state, the information not being shared unless and until it is subsequently determined that the person is in the consenting state.

Embodiments of the disclosure provide a warning method and apparatus for a driving risk, a computing device and a storage medium, and the method includes: obtaining dangerous driving behavior data of a driver in a first time period, and obtaining a correspondence between a quantity of occurrences of dangerous driving behaviors of one or more drivers and a quantity of an actual occurrence of dangerous scenarios to the one or more drivers while driving; predicting, based on a quantity of actual occurrences of the dangerous driving behaviors of the driver, indicated in the dangerous driving behavior data of the driver, and the correspondence, a target quantity of times the driver is predicted to encounter one or more dangerous scenarios in the first time period; and generating warning information based on the target quantity of times.

A method by a computing device obtains a digital image of a physical map, identifies features in the digital image, and obtains map augmentation information based on the identified features. The method then generates an augmented map based on the map augmentation information, and provides the augmented map for display. Related mobile devices and computer program products are disclosed.

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 an image feature of an image; performing first interaction feature extraction on the image feature; processing a plurality of first target features to obtain target information of a plurality of detected targets; processing one or more first interaction features to obtain motion information of a motion, human information of a human target corresponding to each motion, and object information of an object target corresponding to each motion; matching the plurality of detected targets with one or more motions; and updating human information of a corresponding human target based on target information of a detected target matching the corresponding human target, and updating object information of a corresponding object target based on target information of a detected target matching the corresponding object target.

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 an image feature of an image; performing first interaction feature extraction on the image feature; processing a plurality of first target features to obtain target information of a plurality of detected targets; processing one or more first interaction features to obtain motion information of a motion, human information of a human target corresponding to each motion, and object information of an object target corresponding to each motion; matching the plurality of detected targets with one or more motions; and updating human information of a corresponding human target based on target information of a detected target matching the corresponding human target, and updating object information of a corresponding object target based on target information of a detected target matching the corresponding object target.

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.

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.