Abstract: Disclosed are a machine model update method and apparatus, a medium, and a device. The method includes: obtaining first hard example samples of a machine model and attribute information of the first hard example samples; determining category-based data distribution information of the first hard example samples according to the attribute information of the first hard example samples; determining second hard example samples of the machine model in current machine learning according to learning requirement information of the machine model for categories of the first hard example samples and the data distribution information; performing, according to learning operation information corresponding to the machine model, a learning operation on the machine model by using the second hard example samples; and updating the machine model based on a learning result of the machine model. The present disclosure helps efficiently implement a machine learning lifecycle, and reduce the cost of machine learning lifecycle.

Abstract: Disclosed are a behavior recognition method and apparatus, a medium and device. The method includes: obtaining a sequence of image blocks containing a target part of a target object from a plurality of video frames obtained by a camera apparatus disposed in a mobile device; extracting a feature vector on a first preset behavior from the sequence to obtain a first feature vector corresponding to the sequence; determining a behavior type to which a behavior of the target object belongs based on the first feature vector; and if the behavior type to which the behavior belongs satisfies a first behavior type, determining the behavior based on a result of behavior recognition on a second preset behavior for the sequence. The disclosure may realize cascade recognition of the behavior of the target object, thereby helping to improve accuracy of behavior recognition and further driving safety of the mobile device.

Abstract: Disclosed are a fatigue state detection method and apparatus, a medium and a device. The method includes: obtaining image blocks containing an organ area of a target object from a plurality of video frames collected by a camera apparatus disposed in a mobile device, to obtain an image-block sequence that is based on the organ area; determining a fatigue state type of the target object based on the image-block sequence of the organ area; sending the image-block sequence to a cloud server if the fatigue state type meets a first preset type, and rendering the cloud server to detect a fatigue level of the target object based on the image-block sequence; and receiving fatigue level information about the target object that is returned by the cloud server. The present disclosure may improve accuracy of fatigue state detection, thereby helping to improve driving safety of the mobile device.

Abstract: Disclosed are a machine model update method and apparatus, a medium, and a device. The method includes: obtaining first hard example samples of a machine model and attribute information of the first hard example samples; determining category-based data distribution information of the first hard example samples according to the attribute information of the first hard example samples; determining second hard example samples of the machine model in current machine learning according to learning requirement information of the machine model for categories of the first hard example samples and the data distribution information; performing, according to learning operation information corresponding to the machine model, a learning operation on the machine model by using the second hard example samples; and updating the machine model based on a learning result of the machine model. The present disclosure helps efficiently implement a machine learning lifecycle, and reduce the cost of machine learning lifecycle.

Abstract: Disclosed are a behavior recognition method and apparatus, a medium and device. The method includes: obtaining a sequence of image blocks containing a target part of a target object from a plurality of video frames obtained by a camera apparatus disposed in a mobile device; extracting a feature vector on a first preset behavior from the sequence to obtain a first feature vector corresponding to the sequence; determining a behavior type to which a behavior of the target object belongs based on the first feature vector; and if the behavior type to which the behavior belongs satisfies a first behavior type, determining the behavior based on a result of behavior recognition on a second preset behavior for the sequence. The disclosure may realize cascade recognition of the behavior of the target object, thereby helping to improve accuracy of behavior recognition and further driving safety of the mobile device.

Abstract: Disclosed are a fatigue state detection method and apparatus, a medium and a device. The method includes: obtaining image blocks containing an organ area of a target object from a plurality of video frames collected by a camera apparatus disposed in a mobile device, to obtain an image-block sequence that is based on the organ area; determining a fatigue state type of the target object based on the image-block sequence of the organ area; sending the image-block sequence to a cloud server if the fatigue state type meets a first preset type, and rendering the cloud server to detect a fatigue level of the target object based on the image-block sequence; and receiving fatigue level information about the target object that is returned by the cloud server. The present disclosure may improve accuracy of fatigue state detection, thereby helping to improve driving safety of the mobile device.

Abstract: A method for automatic association of commodities and price tags, comprises: S1: acquiring several pictures, carrying out unit detection on each one of the pictures, and identifying a plurality of commodity zones within each one of the pictures; S2: carrying out price tag detection on each one of the pictures, and identifying a plurality of price tag zones within each one of the pictures; S3: identifying a bar code in each one of the price tag zones, reading first commodity information corresponding to each one of the bar codes, and verifying the commodity zone corresponding to the bar code in each one of the price tag zones; S4: associating the commodity zone of each one of the bar codes with the corresponding first commodity information, and then storing the associated data into a database.

Abstract: Embodiments described herein are directed to mechanisms that enable roles (e.g., host vs. function, power provider vs. power consumer, master vs. slave, server vs. client, source vs. sink, upstream vs. downstream) to be dynamically assigned between two interconnected dual-role devices in an intelligent and deterministic manner based on the available context on each device.

Abstract: A method for automatic association of commodities and price tags, comprises: S1: acquiring several pictures, carrying out unit detection on each one of the pictures, and identifying a plurality of commodity zones within each one of the pictures; S2: carrying out price tag detection on each one of the pictures, and identifying a plurality of price tag zones within each one of the pictures; S3: identifying a bar code in each one of the price tag zones, reading first commodity information corresponding to each one of the bar codes, and verifying the commodity zone corresponding to the bar code in each one of the price tag zones; S4: associating the commodity zone of each one of the bar codes with the corresponding first commodity information, and then storing the associated data into a database.

Abstract: Embodiments described herein are directed to mechanisms that enable roles (e.g., host vs. function, power provider vs. power consumer, master vs. slave, server vs. client, source vs. sink, upstream vs. downstream) to be dynamically assigned between two interconnected dual-role devices in an intelligent and deterministic manner based on the available context on each device.

Abstract: Embodiments described herein are directed to mechanisms that enable roles (e.g., host vs. function, power provider vs. power consumer, master vs. slave, server vs. client, source vs. sink, upstream vs. downstream) to be dynamically assigned between two interconnected dual-role devices in an intelligent and deterministic manner based on the available context on each device.

Abstract: Embodiments described herein are directed to mechanisms that enable roles (e.g., host vs. function, power provider vs. power consumer, master vs. slave, server vs. client, source vs. sink, upstream vs. downstream) to be dynamically assigned between two interconnected dual-role devices in an intelligent and deterministic manner based on the available context on each device.