A detection apparatus includes one or more processors. The processors set at least one time-period candidate. The processors input, to a first model that inputs a feature acquired from a plurality of time-series images and the time-period candidate and outputs at least one first likelihood indicating a likelihood of occurrence of at least one action previously determined as a detection target and correction information for acquisition of at least one correction time period resulting from correction of the at least one time-period candidate, the feature and the time-period candidate, and acquire the first likelihood and the correction information output from the first model. The processors detect, based on the at least one correction time period acquired based on the correction information and the first likelihood, the action included in the time-series images and a start time and a finish time of a time period of occurrence of the action.

An own-position estimating device for estimating an own-position of a moving body by matching a feature extracted from an acquired image with a database in which position information and the feature are associated with each other in advance, includes an estimating unit estimating the own-position of the moving body by matching the feature extracted by the extracting unit with the database, and a determination threshold value adjusting unit adjusting a determination threshold value for extracting the feature, in which the determination threshold value adjusting unit acquires the database in a state in which the determination threshold value is adjusted, and adjusts the determination threshold value on the basis of the determination threshold value linked to each of the position information items in the database, and the extracting unit extracts the feature from the image by using the determination threshold value adjusted by the determination threshold value adjusting unit.

An electronic device is provided. The electronic device includes a memory, an image sensor including light receiving elements each including at least two sub light receiving elements, and an image signal processor. The image signal processor is configured to obtain images corresponding to light from outside by using the image sensor, the images including at least a raw image, a first sub image, and a second sub image, the first sub image being an image corresponding to light detected by at least one first sub light, the second sub image being an image corresponding to light detected by at least one second sub light, identify a luminance ratio between the first sub image and the second sub image, identify a defect in the raw image, based on the luminance ratio, and perform a function corresponding to a type of the defect.

An electronic device is provided. The electronic device includes a memory, an image sensor including light receiving elements each including at least two sub light receiving elements, and an image signal processor. The image signal processor is configured to obtain images corresponding to light from outside by using the image sensor, the images including at least a raw image, a first sub image, and a second sub image, the first sub image being an image corresponding to light detected by at least one first sub light, the second sub image being an image corresponding to light detected by at least one second sub light, identify a luminance ratio between the first sub image and the second sub image, identify a defect in the raw image, based on the luminance ratio, and perform a function corresponding to a type of the defect.

An object detection method performed by an object detection apparatus, includes receiving an input image, obtaining, using an object detection model, a result of detecting a target candidate object from the input image, obtaining, using an error prediction model, a result of detecting an error object from the input image, and detecting a target object in the input image based on the result of detecting the target candidate object and the result of detecting the error object.

An object detection method performed by an object detection apparatus, includes receiving an input image, obtaining, using an object detection model, a result of detecting a target candidate object from the input image, obtaining, using an error prediction model, a result of detecting an error object from the input image, and detecting a target object in the input image based on the result of detecting the target candidate object and the result of detecting the error object.

The present disclosure relates to systems and methods for selecting a best facial image of a target human face. The methods may include determining whether a candidate facial image is obtained before a time point in a time period threshold, wherein the candidate facial image has a greatest quality score of the target human face among a plurality of facial images of the target human face; in response to a determination that the candidate facial image is obtained before the time point, determining the candidate facial image as the best facial image of the target human face; and storing the best facial image together with a face ID and the greatest quality score of the target human face in a face log.

Systems, methods, and techniques to efficiently and effectively verifying and calibrating a machine learning model. The method can include training a machine learning model by at least processing training data with the machine learning model. The method can further include manipulating a first data set of the training data and applying the manipulated first data set to the machine learning model to thereby determine a first matching rate. In addition, the method can include applying the manipulated first data set to a rule engine to thereby determine a second matching rate and determining a difference between the first matching rate and the second matching rate. The method can further include determining whether the difference is within a predefined threshold range and providing an error indication if the determined difference is outside of the predefined threshold range.

Systems, methods, and techniques to efficiently and effectively verifying and calibrating a machine learning model. The method can include training a machine learning model by at least processing training data with the machine learning model. The method can further include manipulating a first data set of the training data and applying the manipulated first data set to the machine learning model to thereby determine a first matching rate. In addition, the method can include applying the manipulated first data set to a rule engine to thereby determine a second matching rate and determining a difference between the first matching rate and the second matching rate. The method can further include determining whether the difference is within a predefined threshold range and providing an error indication if the determined difference is outside of the predefined threshold range.

An information processing device according to the present disclosure includes: an acquisition unit that acquires outline information indicating an outline of a user who makes a body motion; and a specification unit that specifies, among body parts, a main part corresponding to the body motion and a related part, which is to be a target of correction processing of motion information corresponding to the body motion, on the basis of the outline information acquired by the acquisition unit.