An object tracking method and apparatus are provided. The object tracking method includes detecting a target object in a first-type input image that is based on light in a first wavelength band, tracking the target object in the first-type input image based on detection information of the target object, measuring a reliability of the first-type input image by comparing the first-type image to an image in a database, comparing the reliability of the first-type input image to a threshold, and tracking the target object in a second-type input image that is based on light in a second wavelength band.

An object tracking method and apparatus are provided. The object tracking method includes detecting a target object in a first-type input image that is based on light in a first wavelength band, tracking the target object in the first-type input image based on detection information of the target object, measuring a reliability of the first-type input image by comparing the first-type image to an image in a database, comparing the reliability of the first-type input image to a threshold, and tracking the target object in a second-type input image that is based on light in a second wavelength band.

A personal authentication device includes an infrared ray emitter that emits infrared rays, an infrared camera that captures an infrared image, a status signal outputter that outputs a signal related to a status of the personal authentication device or the status of surroundings of the personal authentication device, and a controller. The controller determines whether or not there is a predetermined change in the status of the personal authentication device or the status of the surroundings of the personal authentication device based on a signal output from the status signal outputter, and when there is the predetermined change, the controller drives the infrared ray emitter and the infrared camera, and performs a three-dimensional face authentication process based on the infrared image captured by the infrared camera.

In some examples, a method may include receiving retroreflected light that indicates at least one retroreflective property of a retroreflective article, wherein retroreflected light is captured at a first distance. The method may include determining a first set of information based at least in part on the at least one retroreflective property of the retroreflective article. The method may include receiving, from the light capture device, an image that includes at least one object, wherein the image is captured at a second distance. The method may include determining, based at least in part on the spatially resolvable property, a second set of information that corresponds to the object in the image. The method may include performing, by a computing device, at least one operation based at least in part on the second set of information.

In some examples, a method may include receiving retroreflected light that indicates at least one retroreflective property of a retroreflective article, wherein retroreflected light is captured at a first distance. The method may include determining a first set of information based at least in part on the at least one retroreflective property of the retroreflective article. The method may include receiving, from the light capture device, an image that includes at least one object, wherein the image is captured at a second distance. The method may include determining, based at least in part on the spatially resolvable property, a second set of information that corresponds to the object in the image. The method may include performing, by a computing device, at least one operation based at least in part on the second set of information.

The application provides a liveness detection method and a liveness detection system. The liveness detection method includes: obtaining first and second face image data of an object to be detected, and at least one of the first and the second face image data being a depth image; determining a first face region and a second face region, determining whether the first and the second face regions correspond to each other, and extracting, when it is determined that the first and the second face region corresponds to each other, a first and a second face image from the first and the second face region respectively; determining a first classification result for the extracted first face image and a second classification result for the extracted second face image; and determining, based on the first classification result and the second classification result, a detection result for the object to be detected.

Performing optical mark recognition (OMR) scanning includes receiving a substrate to be scanned, the substrate having one or more colors visible to a human eye and bearing user-made marks; moving the substrate and a light sensor relative to each other such that the substrate moves past the light sensor a single time; during the relative movement of the substrate, scanning the substrate using multiple colors of light to simultaneously generate both (a) image data representing a visual appearance of the substrate, and (b) OMR data corresponding to respective locations of the user-made marks on the substrate.

A system for quantifying viewer engagement with a video playing on a display includes at least one camera to acquire image data of a viewing area in front of the display. A microphone acquires audio data emitted by a speaker coupled to the display. The system also includes a memory to store processor-executable instructions and a processor. Upon execution of the processor-executable instructions, the processor receives the image data and the audio data and determines an identity of the video displayed on the display based on the audio data. The processor also estimates a first number of people present in the viewing area and a second number of people engaged with the video. The processor further quantifies the viewer engagement of the video based on the first number of people and the second number of people.

Methods and systems that detect and differentiate epileptogenic from eloquent and normal cortices are provided. A method for identifying epileptogenic cortices in a brain may include detecting areas in the brain that are undergoing cerebral blood volume low frequency oscillations, detecting areas in the brain that are undergoing blood oxygenation low frequency oscillations; mapping clusters of the brain in which the cerebral blood volume low frequency oscillations are negatively correlated with the blood oxygenation low frequency oscillations, and analyzing the time based relationship between the clusters of the brain that are undergoing negatively correlated low frequency oscillations to determine cause areas, which are areas of the brain that are causing negatively correlated low frequency oscillations to occur elsewhere.

A method for depth sensing from an image of a projected pattern is performed at an electronic device with one or more processors and memory. The method includes receiving an image of a projection of an illumination pattern; for a portion of the image, selecting a candidate image of a plurality of candidate images by comparing the portion of the image with a plurality of candidate images; and determining a depth for the portion of the image based on depth information associated with the selected candidate image. Related electronic devices and computer readable storage medium are also disclosed.