There is provided a recognition system adaptable to a portable device or a wearable device. The recognition system senses a body heat using a thermal sensor, and performs functions such as the living body recognition, image denoising and body temperature prompting according to detected results.

An information processing device for a vehicle for sensor data fusion for object detection, including circuitry configured to: obtain, based on obtained first sensor data from a first sensor of the vehicle and a first predetermined object pose probability model, first object pose probability data, wherein the first predetermined object pose probability model is specific for the first sensor; obtain, based on obtained second sensor data from a second sensor of the vehicle and a second predetermined object pose probability model, second object pose probability data, wherein the second predetermined object pose probability model is specific for the second sensor; and fuse the first and the second object pose probability data to obtain fused object pose probability data for object detection.

An example system includes a first camera to capture a first image of a first facial body part and a second camera to capture a second image of a second facial body part. The example system further includes a transformation engine to transform respective scales of the first image and the second image to a scale of full facial images. The example system further includes a local location engine to: identify first facial landmarks and second facial landmarks in respective transformed versions of the first image and the second image, the first facial landmarks and the second facial landmarks used to determine that an action has occurred.

A method, system, and apparatus for an unmanned aerial vehicle (UAV) to autonomously reconstruct overflown terrain and detect safe landing sites. A UAV autonomously acquires on-board pose estimates from an on-board visual-inertial-range odometry method during flight. The on-board pose estimates are utilized as a pose prior and to regain metric scale during three-dimensional (3D) reconstruction. The on-board pose estimates are corrected based on a bundle adjustment approach using previously acquired images. 3D reconstruction is performed based on multiple captured images taken from an on-board camera. Range data from the multiple captured images is fused into a multi-resolution height map. A safe landing site on the terrain is detected based on the multi-resolution height map.

The present teaching relates to method, system, medium, and implementations for detecting liveness. When an image is received with visual information claimed to represent a palm of a person, a region of interests (ROI) in the image that corresponds to the palm is identified. Each of a plurality of fake palm detectors individually generates an individual decision on whether the ROI corresponds to a specific type of fake palm that the fake palm detector is to detect. Such individual decisions from the plurality of fake palm detectors are combined to derive a liveness detection decision with respect to the ROI.

Systems and methods for three-dimensional object counting in a three-dimensional space include an image capture device configured to obtain at least one 2D image of the three-dimensional space, a scanner configured to obtain a 3D point cloud of the three-dimensional space, and a processor configured to cooperate with at least one artificial neural network, the at least one artificial neural network configured to classify and count objects in the three-dimensional space

A method includes determining that an object has fallen to a surface of interest in an interior cabin of a vehicle during a transportation service provided using the vehicle; tracking a location of the object on the surface of interest using at least one interior sensor; updating tracking information for the object, the tracking information including the tracked location of the object and a time at which the location was tracked; inferring a continued existence of the object after the object has moved out of a view of the at least one interior sensor and tagging the object as obscured from view in the tracking information for the object; identifying a user associated with the transportation service; and alerting the identified user to retrieve the object.

The present disclosure discloses an extraction method of raft culture area based on multi-temporal optical remote sensing images, including: constructing a raft culture marker sample library including culture types such as fish, shellfish, and algae; optimizing the deep learning model of the UNet network by using ASPP (Atrous Spatial Pyramid Pooling) and the shape constraint module; using the deep learning model to extract a corresponding multi-temporal raft culture area by using the multi-temporal optical remote sensing images with medium resolution in the target area; combining prior knowledge, fusing the extraction results of the raft culture area to obtain a final extraction results of the raft culture area.

The present disclosure relates to a multi-sensor fusion system and an autonomous mobile apparatus. The multi-sensor fusion system includes: a trigger module including a pulse signal output, the pulse signal output being used to output a pulse signal; and a plurality of depth camera modules, at least one depth camera module including a trigger signal generation module and a trigger signal output, the trigger signal generation module being used to generate a trigger signal according to the pulse signal, and the trigger signal output being connected to the trigger signal generation module, and used to output the trigger signal, where the trigger signal is used for triggering the at least one depth camera module to perform an exposure operation, and other depth camera modules perform exposure operations according to the received trigger signal output by the trigger signal output.

A method, non-transitory computer readable medium, and system that manage agricultural analysis in dynamic environments includes detecting a location of one or more agricultural objects of interest in image data of an environment captured by a sensor device during active navigation of the environment. An orientation and position of the sensor device with respect to the image data is determined. Each of the one or more agricultural objects of interest is analyzed based on the image data, the detected location of the one or more agricultural objects of interest, and the determined orientation and position of the sensor device to determine one or more characteristics about the one or more agricultural objects of interest. At least one action is initiated based on the determined one or more characteristics about the one or more agricultural objects of interest.