Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Boe Technology Groupco., Ltd.

Peking University

Xiaojun Tang

Tong Liu

Xiangyang Liu

Huanhuan Zhang

Yifei Zhang

Naifu Wu

Jingtao Xu

Zhiguo Zhang

Jibo Zhao

Guangwei Huang

Haijun Su

Hui Li

Yue Li

Yu Gu

Xitong Ma

Ran Duan

Bo Xu

Jianwen Suo

Tingting Wang

Hongli Ding

Jinglin Yang

Rui Zheng

Yadong Mu

Jingru Wang

Chunshan Zu

Guangli Wang

Zhanfu An

Yigeng Fang

The present disclosure provides a person flow analysis method and system, an electronic device, and a readable storage medium. The method includes: obtaining a video image of a target area; identifying and tracking target features of persons in the video image; generating person flow data for the target area according to movement states of the target features of the persons relative to a specific position in the target area. By identifying and monitoring the target features in the video images of the target area, it does not need to analyze and identify all features of the persons, thereby reducing the amount of calculation and increasing the scope of application. By analyzing a movement state of the target feature relative to the specific position, movement mode of a user in the target area can be obtained, thereby improving accuracy of analysis of the person flow in the target area.

Person flow analysis method and system, electronic device, and readable storage medium

Provided is a method for recognizing a text. The method includes: acquiring a text image; determining at least one text box in the text image, wherein each of the at least one text box corresponds to at least one word; determining, from the at least one text box, a text box to be recognized; determining a picture unit corresponding to the text box to be recognized in the text image; rotating the picture unit to a target posture; and determining a target recognition result by performing text recognition on the picture unit in the target posture.

Method and apparatus for recognizing text, storage medium, and electronic device

A method for training an object detection model includes: firstly obtaining M sample image sets; then, obtaining an initial object detection model; and finally, training the initial object detection model by using the M sample image sets to obtain the object detection model. A sample image set includes at least one sample image and object type(s) of object(s) in each sample image. An object type corresponds to one sample image set, and the M sample image sets correspond to N object types.

Method for training object detection model, object detection method and apparatus

A distance measurement method is provided. The distance measurement method includes obtaining an image of a target device, the image of the target device including a target object of a first color and a background object of a second color, the first color being different from the second color; processing the image of the target device to obtain a processed image, the processed image including a processed target object of a third color and a processed background object of a fourth color; detecting a contour of the processed target object of the third color; calculating an area encircled by the contour; and calculating a distance between a camera and the target device upon determination of the area encircled by the contour.

Distance measurement method, distance measurement apparatus, and computer-program product

The present application provides a model training method and apparatus, an electronic device and a readable storage medium. The model training method of a pedestrian re-identification network, includes: determining a current weight matrix for multiple identification objects according to a current training result of a neural network model; calculating a difficulty vector corresponding to the multiple identification objects according to the current weight matrix; and, selecting next training samples of the multiple identification objects for a next training according to the difficulty vector.

Model training method and apparatus, electronic device and readable storage medium

A digital artwork display device, a digital artwork management method, and an electronic device are provided. The digital artwork display device includes a registration circuit, a transaction circuit, and a file decryption circuit. The registration circuit is configured to apply for a device identifier and a device public-private key pair, and the device public-private key pair includes a device public key and a device private key. The transaction circuit is configured to acquire a use license, and the use license includes the device identifier and a content key ciphertext obtained by encrypting a content key by using the device public key. The file decryption circuit is configured to decrypt the content key ciphertext in the use license by using the device private key to obtain the content key, and decrypt an encrypted file by using the content key to obtain an original file.

Digital artwork display device, management method, and electronic device

An image-to-image search method which is implemented in a server is provided. The server receives an image to be searched and at least one category of similarity search sent by a terminal. The server extracts a feature code of the image by using a trained hash model that uniquely corresponds to the at least one category of similarity search according to the received image and the at least one category of similarity search. The server obtains target images that are similar to the image according to the feature code of the image in a feature code library that uniquely corresponds to the at least one category of similarity search. The server sends the target images to the terminal.

Image-to-image search method, computer-readable storage medium and server

The present disclosure provides a method of determining a status of a position on a shelf, a shelf and a non-transitory computer-readable storage medium. The method includes: acquiring current detection data of the shelf in a current detection period; determining first status information of respective positions from the current detection data, in response to determining that the current detection data satisfies a preset condition; in response to the current detection data indicating that a target position whose first status information indicates the second status exists, acquiring first status information of the target position from previous detection data in a previous detection period; and determining second status information of the target position based on the first status information of the target position in the current detection data and the previous detection data respectively.

Methods of determining status of position in shelf, shelves and non-transitory computer-readable storage media

Provided are an image processing method, an image matching method, a device, and a storage medium. The image processing method includes: obtaining an image feature of an input image; determining a plurality of local image features of the image feature; determining a plurality of local feature vectors corresponding to the plurality of local image features respectively; determining a hash code of the input image based on the plurality of local feature vectors.

Image processing method with ash code on local feature vectors, image processing device and storage medium

The present disclosure relates to methods related to robot services, related systems, and readable storage media. According to some embodiments of the present disclosure, there is provided a method, including: acquiring images of an area to be served, wherein the area to be served includes serviceable grids obtained by performing equal-area grid division on the area to be served; performing pedestrian recognition on the images to acquire pedestrian heat of each serviceable grid within the preset time period; and marking a serviceable grid with the pedestrian heat exceeding a preset threshold and serviceable grids within a preset range centered on it as grids to be served, obtaining a sub-area to be served composed of the grids to be served.

Method, related system, and readable storage medium related to robot service

The disclosure provides an objective identification device, comprising: a classifier training circuit configured to extract objective characteristics based on training samples and perform offline training based on the objective characteristics to obtain a classifier; and a calculation circuit is configured to identify an objective in an image based on a particle swarm optimization algorithm, wherein each of particles is defined as an object having a predefined size in the image; and a fitness value of each of particles is calculated based on the classifier and the objective characteristics of the particle in the particle swarm optimization algorithm, the fitness value representing a probability that the particle belongs to the objective. The disclosure also provides an objective identification method and driving assistance device. According to the disclosure, not only the identification rate can be increased but also application scenarios having different identification rate requirements can be satisfied.

Device and method of objective identification and driving assistance device

The present application provides an object tracking method. The object tracking method includes obtaining an image of an area in front of a vehicle; dividing the image of the area in the front of the vehicle into a plurality of sub-images; determining a plurality of first sub-images that satisfy a plurality of threshold conditions out of the plurality of sub-images; selecting a plurality of target sub-images out of the plurality of first sub-images, at least one of the plurality of first sub-images is not selected as one of the plurality of target sub-images; and recognizing a target object in one of the plurality of target sub-images.

Object tracking method, object tracking apparatus, vehicle having the same, and computer-program product

The present disclosure provides an image processing method, device and apparatus, and a computer-readable storage medium. The image processing method includes: obtaining an image and detecting multiple targets in the image; obtaining categories of multiple detected targets and first parameters of a detection frame corresponding to each detected target, and ranking all detected targets according to the first parameters in a descending order to obtain a dataset; obtaining a target with a highest first parameter in the dataset as a first detected target, and detecting whether the first parameter of a first detection frame corresponding to the first detected target satisfies a preset category condition of a category to which the first detected target belongs; detecting whether the first detection frame corresponding to the first detected target overlaps with a detection frame corresponding to a respective one of the plurality of detected targets except the first detected target.

Image processing method, device and apparatus for multi-object detection

A commodity detection terminal is disclosed, the commodity detection terminal includes: an image segmentation unit configured to obtain position information of each grid of a goods shelf in an image of the goods shelf based on a first image, which is an image of the goods shelf not placed with commodities; a detector unit configured to obtain a current grid where each commodity is located and a current quantity of each commodity based on a second image, which is a current image of the goods shelf placed with commodities; and a determination unit configured to compare the current grid and the current quantity with a preset grid and a preset quantity of the each commodity, so as to determine whether a status of each commodity satisfies a preset condition. A commodity detection method, an intelligent goods shelf system, a computer device, and a readable medium are also disclosed.

Commodity detection terminal, commodity detection method, system, computer device, and computer readable medium

Embodiments of the disclosure provide an object detection method and device. The object detection method includes: extracting features of an image; classifying the image by each level of classifiers of a cascade classifier according to the features of the image, and calculating a classification score of the image in each level of the classifiers of the cascade classifier according to a classification result; and calculating, according to the classification score, a cascade score of the image in a corresponding level of the cascade classifier, comparing the cascade score in the corresponding level with a cascade threshold of the corresponding level, and judging the presence of an object in the image according to a comparison result.

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Beijing, China

Xiaojun Tang