The present invention relates to a biometric-based identity authentication method and system. The method includes: obtaining mobile terminal numbers of all users entering a specified area through a base station associated with the specified area which the users enter to generate a first mobile terminal number list; recognizing biometrics of users, and obtaining a second mobile terminal number list composed of n mobile terminal numbers with the highest similarity to the biometrics based on a pre-established binding relationship between biometrics of users and the mobile terminal numbers; and comparing the first mobile terminal number list with the second mobile terminal number list, wherein on the condition that the intersection of the two is one mobile terminal number, it is determined that the user of the mobile terminal number is the user with successful identity authentication, and on the condition that the intersection of the two is more than one number, it is determined that the user of the mobile terminal number with the highest biometric similarity in the intersection is the user with successful identity authentication. According to the present invention, the range of face recognition N can be narrowed down, and a user only needs to carry a mobile phone and 1:N face recognition can be completed without additional operations.

One or more multi-stage optimization iterations are performed with respect to a compression algorithm. A given iteration comprises a first stage in which hyper-parameters of a perceptual quality algorithm are tuned independently of the compression algorithm. A second stage of the iteration comprises tuning hyper-parameters of the compression algorithm using a set of perceptual quality scores generated by the tuned perceptual quality algorithm. The final stage of the iteration comprises performing a compression quality evaluation test on the tuned compression algorithm.

Provided may be a method for providing an extended function to a vehicle according to an embodiment, the method comprising the steps of: obtaining first image information required for providing an extended function, through a first photographing unit; obtaining predetermined running information related to the running of a vehicle; performing image processing for providing an extended function, through a first ECU on the basis of the running information and the first image information; and displaying a result of the image processing. Furthermore, provided may be an extended function providing apparatus capable of performing the extended function providing method, and a non-volatile computer-readable recording medium in which a computer program for performing the extended function providing method is contained.

Methods and systems for improved analysis of population data and imagery data for a geographical area are provided. In one embodiment, a method is provided that includes receiving or extracting characteristics for a population and/or a geographical data. The characteristics may be combined with a variable of interest to form an input dataset. A first plurality of machine learning models may be trained based on at least a portion of the input dataset and may be used to generate a plurality of prediction surfaces for the variable of interest. A second plurality of machine learning models may be trained based on the plurality of prediction surfaces and at least one of the second plurality of machine learning models may be selected for future predictions of the variable of interest.

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.

The present disclosure relates to a driving control system and a method of controlling the same using sensor fusion between vehicles, and the driving control system allows vehicles to share sensor data, fuses the sensor data, matches the adjacent vehicle and the sensor data, improves recognition performance in respect to a periphery, controls driving in accordance with a change in peripheral environment and a traveling state of another vehicle, receives sensor data of another vehicle, converts a coordinate of sensor data of the matched vehicle, and fuses host vehicle sensor information, which makes it possible to improve recognition performance and accuracy in respect to a surrounding environment and object, enable stable autonomous driving, and improve stability of the vehicle.

In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

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.

The present invention relates to a multi-channel lidar sensor module capable of measuring at least two target objects using one image sensor. The multi-channel lidar sensor module according to an embodiment of the present invention includes at least one pair of light emitting units configured to emit laser beams and a light receiving unit formed between the at least one pair of emitting units and configured to receive at least one pair of reflected laser beams which are emitted from the at least one pair of light emitting units and reflected by target objects.

This disclosure discloses lane line detection methods and devices. In an implementation, features extracted by different layers of the neural network are fused to obtain a fused second feature map, so that the second feature map obtained through fusion processing has a plurality of layers of features. The fused second feature map has a related feature of a low-layer receptive field and a related feature of a high-layer receptive field. Afterwards, an output predicted lane line set is divided into groups, where each predicted lane line in each group has an optimal prediction interval.