Abstract: An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.

Abstract: Method for estimating a vertical offset of road segment for vehicle is disclosed. The method includes tracking road reference on road segment at first moment in time from first observation point in order to form first representation. Method includes obtaining vehicle movement data over time period extending from first moment in time to second moment in time, tracking road reference on road segment at second moment in time from second observation point in order to form second representation.

Abstract: A margin assessment method is provided. Under cooperation of harmonic generation microscopy (HGM) and a deep learning method, the margin assessment method can instantaneously and digitally determine whether a 3D image group generated by an HGM imaging system is a malignant tumor or the surrounding normal skin, so as to assist in determining margins of a lesion.

Abstract: Examples that relate to calling on a computing device are disclosed. One example provides a computing device, comprising, a first display a second display, a sensor system a logic subsystem and a storage subsystem comprising instructions executable by the logic subsystem to output, via one or both of the first display and the second display, a notification of an incoming phone call at the computing device, and based on output from the sensor system indicating a relative angle between the first display and the second display being changed to an angle greater than a predetermined angle, answer the incoming phone call.

Abstract: Disclosed herein are methods, systems, and computer-readable media for training a machine learning model to label unlabeled data and/or perform automated actions. In an embodiment, a method comprises receiving unlabeled digital video data, generating pseudo-labels for the unlabeled digital video data, the generating comprising receiving labeled digital video data, training an inverse dynamics model (IDM) using the labeled digital video data, and generating at least one pseudo-label for the unlabeled digital video data, wherein the at least one pseudo-label is based on a prediction, generated by the IDM, of one or more actions that mimic at least one timestep of the unlabeled digital video data. In some embodiments, the method further comprises adding the at least one pseudo-label to the unlabeled digital video data and further training the IDM or a machine learning model using the pseudo-labeled digital video data.

Abstract: An antenna module includes a first antenna unit including at least one first patch antenna pattern, at least one first feed via, and at least one first dielectric layer, a second antenna unit including at least one second patch antenna pattern, at least one second feed via, and at least one second dielectric layer, a first connection portion, a first rigid substrate electrically connecting the first connection portion to the first antenna unit and having a first surface on which the first antenna unit is disposed, a base connection portion, a flexible substrate having a first surface on which the first connection portion is disposed and a second surface on which the base connection portion is disposed, and an IC electrically connected to the flexible substrate through the second surface of the flexible substrate or the first rigid substrate.

Abstract: Methods and apparatus provide for: continuously acquiring information of an environment in which at least one of a communication device and a communication partner for the communication device is placed; and upon the acquisition of each information, determining from among a plurality of communication standards of frequency bands of radio waves, and based on the acquired information, the communication standard to be used for communication between the communication device and the communication partner, where the communication partner outputs video and audio signals to the communication device, continuously acquiring includes continuously acquiring the information at a predetermined frame rate, and the communication device includes at least one camera.

Abstract: A face search method and apparatus are provided. The method includes obtaining a to-be-searched face image, and inputting the face image into a first feature extraction model to obtain a first face feature. The method further includes inputting the face image and the first face feature into a first feature mapping model for feature mapping, to output a standard feature corresponding to the first face feature, and performing face search for the face image based on the standard feature. Features extracted by using a plurality of feature extraction models are concatenated, and a concatenated feature is used as a basis for constructing a standard feature.

Abstract: In some instances, a user device for determining whether an individual is sick is provided. The user device is configured to obtain a facial image of an individual; obtain an audio file comprising a voice recording of the individual; determine a facial recognition confidence value associated with whether the individual is sick based on inputting the facial image into a facial recognition machine learning dataset that is individualized for the individual; determine a voice recognition confidence value associated with whether the individual is sick based on inputting the audio file into a voice recognition machine learning dataset that is individualized for the individual; determine whether the individual is sick based on the facial recognition confidence value and the voice recognition confidence value; and causing display of a prompt indicating whether the individual is sick.

Abstract: The present disclosure provide a method and an Internet of Things system for counting and regulating pedestrian volume in a public place of a smart city. The method includes receiving, based on the user platform, a query request for an intended place initiated by a user; transmitting, based on the service platform, the query request to the management platform, and generating, based on the management platform, a query instruction; issuing, based on the management platform, the query instruction to a corresponding sensor network sub-platform according to the regional location; sending, based on the sensor network sub-platform, the query instruction to the corresponding object platform; obtaining, based on the object platform, a query result according to the query instruction; and feeding back, based on the object platform, the query result to the user platform through the corresponding sensor network sub-platform, the management platform, and the service platform respectively.

Abstract: A method, an apparatus, and a computer-readable medium for wireless communication are provided. In some aspects, a base station may send a beam modification command that indicates a set of transmit beam indexes corresponding to a set of transmit beams of a base station, and each transmit beam index of the set of transmit beam indexes may indicate at least a transmit direction for transmitting a transmit beam by the base station. The base station may send, in association with the beam modification command, a reference signal using at least one transmit beam of the set of transmit beams, where a first portion of the reference signal is sent in a first set of symbols and a second portion of the reference signal is received in a second set of symbols.

Abstract: A distracted driver can be informed of his or her distraction by a visual notification. It can be detected whether a driver of a vehicle is focused on a non-critical object located within the vehicle. In response to detecting that the driver of the vehicle is focused on a non-critical object located within the vehicle, an amount of time the driver is focused on the non-critical object can be determined. When the amount of time exceeds a threshold amount of time, a visual notification of distracted driving can be caused to be presented on or visually adjacent to the non-critical object.

Abstract: A method for training a deep learning network for face recognition includes: utilizing a face landmark detector to perform face alignment processing on at least one captured image, thereby outputting at least one aligned image; inputting the at least one aligned image to a teacher model to obtain a first output vector; inputting the at least one captured image a student model corresponding to the teacher module to obtain a second output vector; and adjusting parameter settings of the student model according to the first output vector and the second output vector.

Abstract: Provided is a recognition device that can accurately recognize a position where a vehicle is to be stopped. The recognition device according to the present invention includes: a distance calculation unit 210 that calculates a map information distance between a host vehicle and a target feature based on map information; a stop line detection processing unit 130a that, in a case where the map information distance is equal to or less than a determination value, sets a detection area for detection of the target feature based on the map information distance, detects the target feature within the detection area, and calculates an actually-measured distance between the host vehicle and the target feature; and a stop line position unifying unit 310 that unifies data of the map information distance and data of the actually-measured distance to calculate a unification result of a distance between the host vehicle and the target feature.

Abstract: A smart driving posture control system includes: an image sensor installed in a vehicle, a memory for storing instructions, and a processor connected to the image sensor and the memory. The processor recognizes a user boarding the vehicle through the image sensor to extract human body feature information of the user, extracts recommended posture information based on the extracted human body feature information, and controls a convenience device based on the recommended posture information to adjust a driving posture of the user.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: A method for keypoint matching includes receiving an input image obtained by a sensor of an agent. The method also includes identifying a set of keypoints of the received image. The method further includes augmenting the descriptor of each of the keypoints with semantic information of the input image. The method also includes identifying a target image based on one or more semantically augmented descriptors of the target image matching one or more semantically augmented descriptors of the input image. The method further includes controlling an action of the agent in response to identifying the target.

Abstract: The disclosed computer-implemented method may include (1) receiving a present frame of a video stream, the present frame comprising a present depiction of a multi-segment articulated body system, (2) identifying a previous frame of the video stream that comprises a previous depiction of the multi-segment articulated body system, (3) analyzing the present frame and the previous frame to determine whether the multi-segment articulated body system remained substantially rigid between the previous frame and the present frame, and (4) estimating a pose of the multi-segment articulated body system in the present frame using a first pose estimation computation that treats the multi-segment articulated body system as rigid and that is selected in contrast to a second pose estimation computation based on determining that the multi-segment articulated body system remained substantially rigid between the previous frame and the present frame. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Embodiments may include novel techniques for Task-Adaptive Feature Sub-Space Learning (TAFSSL). For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: training a machine learning system to classify features in images by: generating a sample set comprising one or a few labeled training samples and one or a few additional samples containing instances of target classes, performing dimensionality reduction computed on the samples in the sample set to form a dimension reduced sub-space, generating class representatives in the dimension reduced sub-space using clustering, and classifying features in images using the trained machine learning system.

Abstract: A vehicle external environment recognition apparatus to be applied to a vehicle includes one or more processors and one or more memories configured to be coupled to the one or more processors. The one or more processors are configured to: calculate three-dimensional positions of respective blocks in a captured image; group the blocks to put any two or more of the blocks that have the three-dimensional positions differing from each other within a predetermined range in a group and thereby determine three-dimensional objects; identify each of a preceding vehicle of the vehicle and a sidewall on the basis of the determined three-dimensional objects; and track the preceding vehicle. The one or more processors are configured to determine, upon tracking the preceding vehicle, whether the preceding vehicle to track is to be hidden by the sidewall on the basis of a border line between a blind region and a viewable region.