Abstract: Examples of electronic devices are described herein. In some examples, an electronic device includes a processor to provide a first set of images to an object tracker to output a bounding shape that represents an object in the first set of images. In some examples, the processor is to estimate a size and location of the bounding shape in a second set of images in response to the object tracker losing track of the object in the second set of images.

Abstract: Examples of noise suppression controls are described herein. In some examples, an electronic device includes a processor to classify, using a first machine learning model, an environment based on video of the environment to produce a classification. In some examples, the processor is to detect, using a second machine learning model, a situation in the environment based on the video to produce a detection. In some examples, the processor is to control noise suppression on audio captured from the environment based on the classification and the detection.

Abstract: Examples of electronic devices are described herein. In some examples, an electronic device includes a camera. In some examples, the electronic device also includes a processor to start a lock timer to lock the electronic device in response to detecting that a first person leaves a field of view of the camera. The processor is also to adjust the lock timer in response to detecting a second person within the field of view of the camera.

Abstract: Examples of electronic devices are described herein. In some examples, an electronic device includes a camera to capture a composite image comprising multiple images of a location. In some examples, the electronic device includes a processor to crop a portion of the composite image based on a field-of-view of the camera. In some examples, the processor is to determine whether a neural network identifies the cropped portion. In some examples, the processor is to generate a unique identifier (ID) for the cropped portion in response to the cropped portion being unidentified by the neural network. In some examples, the processor is to associate the unique ID of the cropped portion with the location.

Abstract: Examples of electronic devices are described herein. In some examples, an electronic device includes a camera. In some examples, the electronic device includes a display device. In some examples, the electronic device includes a processor to detect a scene change in a first set of images captured by the camera. The first set of images has a first resolution. The processor is also to detect surveillance of the display device in response to detecting the scene change. The surveillance detection is based on a second set of images captured by the camera. The second set of images has a second resolution that is higher than the first resolution.

Abstract: A method of determining a region of interest may include with a first image capturing device, determining a region of interest within a field of view common between the first image capturing device and a second image capturing device, the first image capturing device capturing an image at a lower resolution than the second image capturing device; determining a corner defining the region of interest; and upscaling the determined corner to match a corner within a field of view of an image captured by the second image capturing device.

Abstract: A multiple-pattern fiducial for a heterogeneous imaging sensor system. The multiple-pattern fiducial includes a first pattern detectable by a first wavelength of electromagnetic energy, and a second pattern detectable by a second wavelength of electromagnetic energy.

Abstract: An example system includes a pose engine to determine a pose of a camera based on an environmental measurement. The system also includes a transform engine to determine a transform to apply to frames captured by the camera based on the pose of the camera. The system includes a correction engine to apply the transform to the frames captured by the camera in substantially real-time with capturing of the frames.

Abstract: An example system includes a camera aimable at a planar platform, a depth sensor aimable at the planar platform, and a controller to control the camera to obtain a captured image of the planar platform. The controller is further to control the depth sensor to capture depth data of the planar platform. The controller is further to determine a pose of the camera using the depth data and features extracted from the captured image.

Abstract: A multiple-pattern fiducial for a heterogeneous imaging sensor system. The multiple-pattern fiducial includes a first pattern detectable by a first wavelength of electromagnetic energy, and a second pattern detectable by a second wavelength of electromagnetic energy.

Abstract: An example system includes a plurality of cameras to capture images of a target object and a controller to connect to the plurality of cameras. The controller is to control the plurality of cameras to capture the images of the target object. The controller is further to determine a pose of a camera of the plurality of cameras. The system further includes a platform to support the target object. The platform includes a plurality of unique markers arranged in a predetermined layout. The controller is further to determine the pose of the camera based on unique markers of the plurality of unique markers that are detected in an image captured by the camera.

Abstract: A method of determining a region of interest may include with a first image capturing device, determining a region of interest within a field of view common between the first image capturing device and a second image capturing device, the first image capturing device capturing an image at a lower resolution than the second image capturing device; determining a corner defining the region of interest; and upscaling the determined corner to match a corner within a field of view of an image captured by the second image capturing device.

Abstract: An example system includes a pose engine to determine a pose of a camera based on an environmental measurement. The system also includes a transform engine to determine a transform to apply to frames captured by the camera based on the pose of the camera. The system includes a correction engine to apply the transform to the frames captured by the camera in substantially real-time with capturing of the frames.