To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.

The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.

A method for driver identification including recording a first image of a vehicle driver; extracting a set of values for a set of facial features of the vehicle driver from the first image; determining a filtering parameter; selecting a cluster of driver identifiers from a set of clusters, based on the filtering parameter; computing a probability that the set of values is associated with each driver identifier of the cluster; determining, at the vehicle sensor system, driving characterization data for the driving session; and in response to the computed probability exceeding a first threshold probability: determining that the new set of values corresponds to one driver identifier within the selected cluster, and associating the driving characterization data with the one driver identifier.

A circuit system and a method of analyzing audio or video input data that is capable of detecting, classifying, and post-processing patterns in an input data stream. The circuit system may consist of one or more digital processors, one or more configurable spiking neural network circuits, and digital logic for the selection of two-dimensional input data. The system may use the neural network circuits for detecting and classifying patterns and one or more the digital processors to perform further detailed analyses on the input data and for signaling the result of an analysis to outputs of the system.

The driving assistance system includes an imaging device capable of capturing a first monochrome image in a vehicle traveling direction, a first neural network for segmentation processing, a second neural network for depth estimation processing, a determination portion determining a center of a portion cut off from the first monochrome image on the basis of the segmentation processing and the depth estimation processing, a third neural network for colorization processing of only a second cut-off monochrome image, and a display device for enlargement of the second monochrome image subjected to the colorization processing.

A flight plan generation device for generating a flight plan of a flight device that transmits a captured image captured by an imaging unit through wireless communication during flight includes a first acquisition unit configured to acquire target information for specifying an imaging target to be imaged by the imaging unit during flight of the flight device and image quality information for specifying required image quality of a captured image of the imaging target, a second acquisition unit configured to acquire communication quality information on wireless communication quality in a predetermined area including at least a flight airspace of the flight device, and a flight plan generation unit configured to generate a flight plan including a flight path of the flight device and an imaging parameter of the imaging unit based on the acquired target information, image quality information, and communication quality information.

A system and method for continuous real-time assessment of the situational awareness of an aircraft operator incorporates gaze sensors to determine the current gaze target (or sequence of gaze targets) of the operator, e.g., which interfaces the operator is looking at. The system receives operational context from aircraft systems indicative of current events and conditions both internal and external to the aircraft (e.g., operational status, mission or flight plan objectives, weather conditions). Based on the determined gaze targets and coterminous operational context, the system evaluates the situational awareness of the operator relative to the operational context, e.g., perceptive of the operational context; comprehending the operational context and its implications, and projecting the operator's perception and comprehension into responsive action and second-order ramifications according to task models indicative of expected behavior.

This invention provides a system and method that uses a hybrid model for transportation-based (e.g. maritime) visual event detection of events. In operation, video data is reduced by detecting change and exclusively transmitting images to the deep learning model when changes are detected, or alternatively, based upon a timer that samples at selected intervals. Relatively straightforward deep learning models are used, which operate on sparse individual frames, instead of employing complex deep learning models that operate on multiple frames/videos. This approach reduces the need for specialized models. Independent, rule-based classifiers are used, based on the output of the deep learning model into visual events that, in turn, allows highly specialized events to be constructed. For example, multiple detections can be combined into higher-level single events, and thus, the existence maintenance procedures, cargo activities, and/or inspection rounds can be derived from combining multiple events or multiple detections.

This invention provides a system and method that uses a hybrid model for transportation-based (e.g. maritime) visual event detection of events. In operation, video data is reduced by detecting change and exclusively transmitting images to the deep learning model when changes are detected, or alternatively, based upon a timer that samples at selected intervals. Relatively straightforward deep learning models are used, which operate on sparse individual frames, instead of employing complex deep learning models that operate on multiple frames/videos. This approach reduces the need for specialized models. Independent, rule-based classifiers are used, based on the output of the deep learning model into visual events that, in turn, allows highly specialized events to be constructed. For example, multiple detections can be combined into higher-level single events, and thus, the existence maintenance procedures, cargo activities, and/or inspection rounds can be derived from combining multiple events or multiple detections.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.