An information processing apparatus includes an input interface, a processor, and an output interface. The input interface obtains observation data obtained from an observation space. The processor detects a subject image of a detection target from the observation data, calculates a plurality of individual indices indicating degrees of reliability, each of which relates to at least identification information or measurement information regarding the detection target, and also calculates an integrated index, which is obtained by integrating a plurality of calculated individual indices. The output interface outputs the integrated index.

An information processing apparatus includes an input interface, a processor, and an output interface. The input interface obtains observation data obtained from an observation space. The processor detects a subject image of a detection target from the observation data, calculates a plurality of individual indices indicating degrees of reliability, each of which relates to at least identification information or measurement information regarding the detection target, and also calculates an integrated index, which is obtained by integrating a plurality of calculated individual indices. The output interface outputs the integrated index.

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

The present technology relates to an information processing apparatus, an information processing method, and a program that allow a sensing time of a sensor to be easily and accurately determined. An information processing apparatus includes a control circuit that outputs a control signal for controlling a sensing timing of a sensor, a counter that updates a counter value in a predetermined cycle, and an addition circuit that adds, to sensor data output from the sensor, sensing time information including a first counter value, a second counter value, and a GNSS (Global Navigation Satellite System) time in a GNSS. The first counter value is obtained when the control signal is output from the control circuit, and the second counter value is obtained when a pulse signal synchronous with the GNSS time is output from a GNSS receiver. The present technology can be applied to, for example, a vehicle-mounted camera.

In variants, a system management platform can include a set of system representations and a set of platform-standard element models. Each system representation can include a set of component representations related by a set of constraint representations 140, which can represent the sensing components of a system and the relationships therebetween, respectively, and store component-specific and constraint-specific calibration parameter values, respectively. The component representations 120 can optionally reference the element models.

A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

A sensor aiming device includes: a target positional relationship processing unit for outputting positional relationship information of first and second targets; a sensor observation information processing unit configured to convert the observation result of the first and second targets into a predetermined unified coordinate system according to a coordinate conversion parameter, perform time synchronization at a predetermined timing, and extract first target information indicating a position of the first target and second target information indicating a position of the second target; a position estimation unit configured to estimate a position of the second target using the first target information, the second target information, and the positional relationship information; and a sensor correction amount estimation unit configured to calculate a deviation amount of the second sensor using the second target information and an estimated position of the second target and estimate a correction amount.

A behavior recognition method and system, including: dividing video data into a plurality of video clips, performing frame extraction processing on each video clip to obtain frame images, and performing optical flow extraction on the frame images to obtain optical flow images; performing feature extraction on the frame images and the optical flow images to obtain feature maps of the frame images and the optical flow images; performing spatio-temporal convolution processing on the feature maps of the frame images and the optical flow images, and determining a spatial prediction result and a temporal prediction result; fusing the spatial prediction results of all the video clips to obtain a spatial fusion result, and fusing the temporal prediction results of all the video clips to obtain a temporal fusion result; and performing two-stream fusion on the spatial fusion result and the temporal fusion result to obtain a behavior recognition result.

A behavior recognition method and system, including: dividing video data into a plurality of video clips, performing frame extraction processing on each video clip to obtain frame images, and performing optical flow extraction on the frame images to obtain optical flow images; performing feature extraction on the frame images and the optical flow images to obtain feature maps of the frame images and the optical flow images; performing spatio-temporal convolution processing on the feature maps of the frame images and the optical flow images, and determining a spatial prediction result and a temporal prediction result; fusing the spatial prediction results of all the video clips to obtain a spatial fusion result, and fusing the temporal prediction results of all the video clips to obtain a temporal fusion result; and performing two-stream fusion on the spatial fusion result and the temporal fusion result to obtain a behavior recognition result.