The present invention provides a method of a control circuit for controlling a fingerprint sensing operation of a display panel. The fingerprint sensing operation includes a reset operation, an exposure operation and a sample operation. The method includes steps of: performing the reset operation, the exposure operation and the sample operation of a fingerprint sensing cycle during a display frame interval in which a polarity of display data voltage on the display panel remains unchanged; and adjusting an exposure time of the exposure operation to be within the display frame interval.

In one aspect, a retail store includes a multitude of cameras, including a plurality of 3D cameras, and a plurality of other cameras. Certain of the cameras provide imagery from which a shopper's track through the store is monitored, and certain of the cameras are positioned to detect removal of items from store shelves. The store also includes a computer system that provides a database of information about store layout, indicating stock locations of different items. The computer system receives imagery from the cameras (or information derived from such imagery) and uses this data, together with information from the database and information derived from other sensors in the store, to produce a probabilistic tally of items selected by a store shopper. This tally includes an item bearing a barcode, but is produced without reading the barcode. Each item on the tally is associated with a confidence score that meets a computer system-determined threshold. A great number of other features and arrangements are also detailed.

In one aspect, a retail store includes a multitude of cameras, including a plurality of 3D cameras, and a plurality of other cameras. Certain of the cameras provide imagery from which a shopper's track through the store is monitored, and certain of the cameras are positioned to detect removal of items from store shelves. The store also includes a computer system that provides a database of information about store layout, indicating stock locations of different items. The computer system receives imagery from the cameras (or information derived from such imagery) and uses this data, together with information from the database and information derived from other sensors in the store, to produce a probabilistic tally of items selected by a store shopper. This tally includes an item bearing a barcode, but is produced without reading the barcode. Each item on the tally is associated with a confidence score that meets a computer system-determined threshold. A great number of other features and arrangements are also detailed.

A system and method detects falling incidents on structures such as cruise vessels, oil rigs, overpasses, and buildings, and also detects overboarding movements onto structures such as cargo ships. The system includes at least two opposed imaging devices which record video streams of a detection cuboid within an overlapping region of view volumes for the imaging devices. The imaging devices monitor objects that pass through the cuboid. Identified objects within the video streams are paired, their conformance is determined, and real-world information such as size, trajectory, and location is determined.

Techniques for analysis and deep learning modeling of sensor-based object detection data in bounded aquatic environments are described, including capturing an image from a sensor disposed substantially above a waterline, the sensor being housed in a structure electrically coupled to a light housing, converting the image into data, the data being digitally encoded, evaluating the data to separate background data from foreground data, generating tracking data from the data after the background data is removed, the tracking data being evaluated to determine whether a head or a body are detected by comparing the tracking data to classifier data, tracking the head or the body relative to the waterline if the head or the body are detected in the tracking data, and determining a state associated with the head or the body.

Techniques for analysis and deep learning modeling of sensor-based object detection data in bounded aquatic environments are described, including capturing an image from a sensor disposed substantially above a waterline, the sensor being housed in a structure electrically coupled to a light housing, converting the image into data, the data being digitally encoded, evaluating the data to separate background data from foreground data, generating tracking data from the data after the background data is removed, the tracking data being evaluated to determine whether a head or a body are detected by comparing the tracking data to classifier data, tracking the head or the body relative to the waterline if the head or the body are detected in the tracking data, and determining a state associated with the head or the body.

Methods and devices related to actuating an image sensor are described. In an example, a method can include generating a first image of an area at a first time using an array of image sensors, wherein each image sensor of the array of image sensors is coupled to a respective actuator, generating a second image of the area at a second time using the array of image sensors, comparing the first image to the second image using a processing resource coupled to the array of image sensors, identifying a moving object based at least in part on comparing the first image to the second image, and activating the actuator for each image sensor of the array of image sensor used to generate the first and second images based at least in part on identifying the moving object.

In one embodiment, an infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including an optical focal plane array (FPA) unit. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. Said optical system and said processing unit can be contained together in a data acquisition and processing module configured to be worn or carried by a person.

A video monitoring system includes a camera head, including an infrared illumination source and an image sensor. A mount is configured to hold the camera head in a fixed location and orientation above a crib, so that the image sensor captures images of the crib and an intervention region adjacent to the crib from a fixed perspective.

In one aspect, a retail store has multiple sensors, including item sensors in a shopping cart for gathering data from a shopper-selected first item. At least certain of the sensor data is provided to a classifier, which was previously-trained (using data including optical data from known items) to identify possible item matches corresponding to data sensed from the first item. An item identification hypothesis that the shopper-selected first item has a particular identity is evaluated based on (a) information from the classifier, and (b) store layout data indicating items associated with a store location visited by the cart or shopper. The item identification hypothesis has a confidence score. If the score meets a criterion, an item of the hypothesized identity is added to a shopping tally. A great number of other features and arrangements are also detailed.