Abstract: A method, device, computer equipment and storage medium for GNSS time synchronization are disclosed. The method includes: receiving data packet of NMEA protocol, reading a valid UTC time from data packet of NMEA protocol, and storing the read valid UTC time in a time synchronization controller; receiving PPS signal, capturing a local time output by local clock when PPS signal is generated, and storing the local time in the time synchronization controller; reading a last local time stored before the current local time and reading the stored latest UTC time as a UTC time corresponding to the last local time when the time synchronization controller receives the current local time; and determining, by the time synchronization controller, a local time correction amount according to the last local time and the UTC time corresponding to the last local time, and correcting the local clock according to the local time correction amount.

Abstract: A method and an apparatus for generating a three-dimensional point cloud image, a computer device and a storage medium. The method comprises: detecting three-dimensional shape coordinates of a three-dimensional object in an infrared camera coordinate system; detecting three-dimensional marker coordinates of the marker set in a marker measurement coordinate system at the same time; obtaining, according to a pre-stored first conversion relationship and a second conversion relationship parameter, three-dimensional color coordinates in the infrared camera coordinate system on the basis of the three-dimensional marker coordinates in the marker measurement coordinate system; and generating a three-dimensional point cloud image of the three-dimensional object in the infrared camera coordinate system by combining the three-dimensional shape coordinates and the three-dimensional color coordinates of the three-dimensional object in the infrared camera coordinate system.

Abstract: An event camera system for pupil detection may include a camera assembly and a controller, and may also include one or more off-axis light sources. The camera assembly may include one or more infrared (IR) light sources and an event camera. The one or more IR light sources are configured to emit pulses of IR light along an optical path toward an eyebox. The IR light is reflected from an eye in the eyebox, and the reflected light propagates back along the optical path toward the event camera for detection. The controller is configured to determine an orientation of the eye using data output from the event camera.

Abstract: A method performed by an electronic device with one or more processors and memory includes receiving a plurality of frames of an image; selecting one frame of the plurality of frames as a main frame thereby leaving the rest of the plurality of frames as reference frames; aligning the reference frames with the main frame; determining fusion weights for a respective reference frame of the reference frames by comparing the main frame and the respective reference frame; and obtaining a weighted combination of the main frame and the reference frames based on the fusion weights. An electronic device configured for performing the method and a computer readable storage medium storing instructions for performing the method are also disclosed.

Abstract: The disclosure provides a processing system which comprises a plurality of processors independently executing instructions; a plurality of registers arranged in a manner corresponding to the plurality of processors. Each of the plurality of registers is configured to set first register bits for other processors except the corresponding processor, and the other processors can write the first register bits set for them to indicate event requests. And each processor of the plurality of processors is configured to learn the event requests from the remaining processors for it by reading the first register bits of the corresponding register. Therefore, the application establishes an inter-processor communication module for a plurality of processors in a processing system.

Abstract: A safety belt detection method, apparatus, computer device and computer readable storage medium are disclosed. The safety belt detection method includes the steps as follows. An image to be detected is obtained. The image to be detected is inputted into a detection network which includes a global dichotomous branch network and a grid classification branch network. A dichotomous result, which indicates whether a driver is wearing a safety belt and is output from the global dichotomous branch network, is obtained. A grid classification diagram, which indicates a position information of the safety belt and is output from the grid classification branch network, is obtained based on image classification. A detection result of the safety belt, indicating whether the driver is wearing the safety belt normatively, is obtained based on the dichotomous result and the grid classification diagram.

Abstract: The present invention discloses an intelligent cloud-based photo enhancement system to improve the quality and aesthetics of an image captured via an electronic device. The cloud-based photo enhancement system comprises a cloud database containing a collection of high-quality photos taken at various locations worldwide and under different environment settings. Information from photos stored in the cloud database taken using better photographic capability cameras is used to improve details that the new photo fails to capture. The system relies not only on machine learning models but also utilizes data associated with the new image collected from the hardware of the electronic device such as camera settings, GPS and phone sensors, which results in more reliable information.

Abstract: A method of human pose estimation, including, receiving an image frame, extracting in at least a first stage a first stage image feature based on the image frame, extracting in at least a second stage a second stage image feature based on the first stage image feature, extracting in at least a subsequent stage a subsequent stage image feature based on the second stage image feature, up-sampling the subsequent stage image feature, up-sampling the second stage image feature, concatenating the first stage image feature, the up-sampled second stage image feature and the up-sampled subsequent stage image feature and outputting a feature map based on an output of the concatenation.

Abstract: A video encoding/decoding method and apparatus, a computer device, and a storage medium. The method comprises: for each non-key frame in a video frame sequence, dividing a current non-key frame into a plurality of sub-image blocks according to information of an object in the current non-key image, and determining an importance level of each sub-image block; according to a pre-stored first correlation between different importance levels and different resolutions, performing conversion to make each sub-image block in each non-key frame have a resolution corresponding to the importance level of the sub-image block, wherein in the first correlation, a higher importance level corresponds to a higher resolution, and the highest importance level corresponds to a target highest resolution; and performing video encoding on the video frame sequence to obtain encoded video data. By means of the method, memory and bandwidth occupied by a video can be reduced.

Abstract: The application relates to a method, a device and a storage medium for storing and playing back unmanned driving information, wherein the unmanned driving information storage method comprises the following steps of: acquiring a plurality of data signals associated with an abnormal event when the abnormal event of an unmanned vehicle is monitored; calculating a first hash value for each data signal; storing each first hash value and the corresponding data signal as a key value pair in a key value database respectively; generating a first Merkel tree based on each first hash value; and storing the first Merkel tree and critical data signals of the plurality of data signals into a blockchain, and generating corresponding electronic money addresses and transaction timestamps. By this method, the correctness, integrity, fairness, and credibility of abnormal field data can be guaranteed, and too much storage space will not be occupied at the same time.

Abstract: The present invention discloses a liveliness detection technique. The technique is described for identifying facial attributes. The technique identifies the presented face in the image as real or deceptive. The system and method includes identifying the facial attributes and utilizing a multi task learning network. The neural network includes segmentation and classification functionalities. The final output is used to get pixel level semantic information and high level semantic information.

Abstract: The disclosure provides a bus-based communication system, which comprises a bus controller, a first buffer, a bus redundant controller, a second buffer and a comparator. The bus controller is configured to receive a bus input signal and send a bus output signal. The first buffer is connected with the bus controller in parallel to receive the bus input signal, and is configured to output the bus input signal after a preset time, wherein the preset time is two or more duty cycles of the bus controller. The bus redundant controller is connected to an output terminal of the first buffer and configured to receive a bus output signal delayed by at least T cycles output by the first buffer and output a bus redundant output signal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model configured to generate a predicted depth image, comprising receiving data representing training samples that include a plurality of image pairs, each image pair includes a target image and a reference image both capturing a particular scene from different orientations; for each of the plurality of image pairs, generating a compressed cost volume for the image pair; providing the compressed cost volume as an input to the machine learning model; generating, using the machine learning model, output data representing a predicted disparity map for the compressed cost volume; and generating a total loss using the predicted disparity map for the compressed cost volume, the total loss includes a boundary loss, an occlusion loss, and a transfer loss; and updating the plurality of parameters of the machine learning model by minimizing the total losses.

Abstract: Embodiments of this disclosure provide a system and method for reducing quantization errors at an output of a hardware device functioning as a neural network structure. During operation, the system can obtain type information associated with the neural network structure and construct a hardware model of the neural network structure based on the obtained type information, the hardware model comprising one or more paths for performing arithmetic operations. The system can formulate an optimization problem to reduce the quantization errors based on the constructed hardware model, the optimization problem being defined by an objective function and a set of constraints. The system can solve the optimization problem and configure the hardware device based on a solution to the optimization problem, thereby reducing the quantization errors at the output of the hardware device.

Abstract: An advanced driver assistance system and an inter-core communication device are provided. The inter-core communication device includes an original file layer, a bottom tool layer and an upper tool layer. The original file layer includes an IPC communication interface, a Franca IDL file, a Franca specification file and a Franca definition file. The Franca IDL file is used to describe an interface definition language file of the IPC communication interface. The Franca specification file is used to declare a target communication interface and attributes of the target communication interface. The Franca definition file is adapted for defining the Franca IDL file. The bottom tool layer is adapted for generating a first model object, a second model object and a third model object. The upper tool layer is adapted for generating a target communication interface between a target client and a target server.

Abstract: A mobile generator includes a housing having a top portion, a first side portion, and a second side portion, wherein the first side portion includes an energy-receiving component. The mobile generator also includes an arm pivotally coupled to the housing at a first pivot point and to the first side portion at a second pivot point. A first motion controller is configured to drive rotation of the arm about the first pivot point, and a second motion controller configured to drive rotation of the first side portion about the second pivot point.

Abstract: One embodiment provides a system and method for generating vectorized maps. During operation, the system can obtain labeled training samples comprising images and vectorized ground truth information associated with the images. The system can generate, based on a machine learning model, a prediction output for each training sample, the prediction output comprising a vectorized map. The system can apply a differentiable rasterization technique to a vectorized map element within the vectorized map to obtain a segmentation mask, apply the differentiable rasterization technique to the vectorized ground truth information associated with the vectorized map element to obtain a ground truth segmentation mask, compute a segmentation loss function based on the segmentation mask and the ground truth mask, and update the machine learning model based on the segmentation loss function, thereby facilitating optimized generation of vectorized maps.

Abstract: A CPU resource control method and apparatus, and a computer-readable storage medium are provided. The method includes: detecting CPU resources occupied by a plurality of processes in a virtual machine; taking a process among the plurality of processes that occupies the CPU resources over a preset threshold as a target process required to adjust a CPU to be used, and calculating, according to the number of times the target process has been adjusted, the number of CPUs to be used by the target process, as the number of times the target process has been adjusted increases, the number of CPUs to be used by the target process decreases until it reaches 1; no longer detecting the target process when the number of CPUs to be used by the target process is 1; and selecting at least one CPU from the plurality of CPUs for use by the target process.