Abstract: Aspects of the subject disclosure may include, for example, a machine-readable storage medium, including executable instructions that, when executed by a processing system including a processor, facilitate performance of operations that include assuming control of a video content transfer between a video server and a video client for HTTP traffic responsive to determining the HTTP traffic is for video content, generating a video transfer rate selection and a video transfer resolution selection for a requested video chunk of a time slot of the video content, generating an optimized manifest file for the requested video chunk, and publishing the optimized manifest file to the video client, wherein the video client request the requested video chunk from the video server based on the optimized manifest. Other embodiments are disclosed.

Abstract: 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.

Abstract: 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.

Abstract: The embodiments of the present disclosure disclose a method and apparatus for extracting an edge of an object in a depth image, a storage medium, and a computer device, to provide a solution which is simple to implement and accurate in edge extraction. The method for extracting an edge of an object in a depth image includes: calculating at least two sets of edge pixels of a first depth image using at least two edge detection algorithms; and acquiring a final set of edge pixels by combining the at least two sets of edge pixels. The solution according to the embodiments of the present disclosure is simple to implement, and may obtain a more accurate set of edge pixels, thereby obtaining an accurate pixel-level segmentation result.

Abstract: A method for tracking an at least partially occluded object. The method includes recognizing a non-occluded portion of the at least partially occluded object in an input image; generating a simulated image of the at least partially occluded object based on features of the non-occluded portion extracted from the input image; determining first coordinates of the at least partially occluded object in a first coordinate system; and converting the first coordinates of the at least partially occluded object in the first coordinate system into second coordinates in a second coordinate system defined in a display apparatus.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, a machine-readable storage medium, including executable instructions that, when executed by a processing system including a processor, facilitate performance of operations that include assuming control of a video content transfer between a video server and a video client for HTTP traffic responsive to determining the HTTP traffic is for video content, generating a video transfer rate selection and a video transfer resolution selection for a requested video chunk of a time slot of the video content, generating an optimized manifest file for the requested video chunk, and publishing the optimized manifest file to the video client, wherein the video client request the requested video chunk from the video server based on the optimized manifest. Other embodiments are disclosed.

Abstract: The present disclosure provides a line segment detection method, apparatus, device, and a computer-readable storage medium. Said method includes: extracting image features of an image to be processed, the image features including an image gradient angle and an image gradient amplitude; determining a plurality of seed points based on the extracted image features; for each seed point of the plurality of seed points, determining a current connected region of respective seed point according to a region growth rule, wherein the region growth rule needs to satisfy both a gradient amplitude growth rule and a gradient angle growth rule simultaneously; and performing line segment fitting on line segments within the current connected region of respective seed point.

Abstract: A method of encoding a watermark into a digital image is provided. The method includes partitioning an image into a plurality of blocks of a same size; accumulating the plurality of blocks of the same size into a single block image; performing a Fourier transformation on the single block image to obtain a two-dimensional Fourier spectrum defined by Fourier coefficients at different positions of a Fourier domain; inserting a watermark into a frequency domain of the two-dimensional Fourier spectrum by modifying the two-dimensional Fourier spectrum as a function of watermarking coefficients in the watermark, to obtain a modified Fourier spectrum; performing an inverse Fourier transformation on the modified Fourier spectrum to obtain a watermarked image; copying the watermarked image horizontally and vertically into a plurality of copied watermarked images; and splicing the plurality of copied watermarked images into a reconstituted watermark image.

Abstract: The present disclosure discloses a sample acquisition method, a target detection model generation method, a target detection method, a computing device, and a computer readable medium. The sample acquisition method includes: adding a perturbation to a pre-marked sample original box in an original image to obtain a sample selection box, wherein an image framed by the sample original box contains a target; and extracting an image framed by the sample selection box as a sample. The technical solutions of the present disclosure can effectively increase the number of the samples that can be acquired in the original image, and adding a background to the samples can effectively improve the recognition accuracy of the trained target detection model.

Abstract: A computer-implemented method of detecting a foreign object on a background object in an image is provided. The computer-implemented method includes extracting image features of the image based on image characteristics of the background object and a suspected foreign object; detecting a salient region in the image based on a human visual attention model; generating a salient region mask based on detection of the salient region; obtaining the image features in a region surrounded by the salient region mask; perforating feature combination and feature vector length normalization on the image features of the region surrounded by the salient region mask to generate normalized feature vectors; and identifying the suspected foreign object using an image classifier, wherein the image classifier uses the normalized feature vectors as input for identifying the suspected foreign object.

Abstract: The present disclosure relates to a method and an apparatus for measuring a distance using a vehicle-mounted camera, a storage medium, and an electronic device. The method includes: calibrating a camera based on camera calibration images to obtain a camera parameter; detecting parallel lane lines to obtain a vanishing point of the parallel lane lines according to the detected parallel lane lines; calculating a pitch angle of the camera according to the camera parameter and the vanishing point; determining information of an object to be detected in an image captured by the camera; and calculating a distance from the object to be detected to the camera and a size of the object to be detected according to the information of the object to be detected, the pitch angle of the camera, and the camera parameter.

Abstract: 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.

Abstract: The embodiments of the present disclosure disclose a method and apparatus for extracting an edge of an object in a depth image, a storage medium, and a computer device, to provide a solution which is simple to implement and accurate in edge extraction. The method for extracting an edge of an object in a depth image includes: calculating at least two sets of edge pixels of a first depth image using at least two edge detection algorithms; and acquiring a final set of edge pixels by combining the at least two sets of edge pixels. The solution according to the embodiments of the present disclosure is simple to implement, and may obtain a more accurate set of edge pixels, thereby obtaining an accurate pixel-level segmentation result.

Abstract: A method of encoding a watermark into a digital image is provided. The method includes partitioning an image into a plurality of blocks of a same size; accumulating the plurality of blocks of the same size into a single block image; performing a Fourier transformation on the single block image to obtain a two-dimensional Fourier spectrum defined by Fourier coefficients at different positions of a Fourier domain; inserting a watermark into a frequency domain of the two-dimensional Fourier spectrum by modifying the two-dimensional Fourier spectrum as a function of watermarking coefficients in the watermark, to obtain a modified Fourier spectrum; performing an inverse Fourier transformation on the modified Fourier spectrum to obtain a watermarked image; copying the watermarked image horizontally and vertically into a plurality of copied watermarked images; and splicing the plurality of copied watermarked images into a reconstituted watermark image.

Abstract: 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.

Abstract: A method for tracking an at least partially occluded object. The method includes recognizing a non-occluded portion of the at least partially occluded object in an input image; generating a simulated image of the at least partially occluded object based on features of the non-occluded portion extracted from the input image; determining first coordinates of the at least partially occluded object in a first coordinate system; and converting the first coordinates of the at least partially occluded object in the first coordinate system into second coordinates in a second coordinate system defined in a display apparatus.

Abstract: A computer-implemented method of detecting a foreign object on a background object in an image is provided. The computer-implemented method includes extracting image features of the image based on image characteristics of the background object and a suspected foreign object; detecting a salient region in the image based on a human visual attention model; generating a salient region mask based on detection of the salient region; obtaining the image features in a region surrounded by the salient region mask; perforating feature combination and feature vector length normalization on the image features of the region surrounded by the salient region mask to generate normalized feature vectors; and identifying the suspected foreign object using an image classifier, wherein the image classifier uses the normalized feature vectors as input for identifying the suspected foreign object.

Abstract: According to the embodiments of the present disclosure, there are provided a method and device for identifying an object and a computer readable storage medium. The method includes: determining candidate categories of the object using a first neural network; and determining, in response to the candidate categories of the object having been determined, a category of the object using second neural networks respectively corresponding to the candidate categories. The device includes: a processor; a memory having stored thereon instructions which, when executed by the processor, cause the processor to: determine candidate categories of the object using a first neural network; and determine, in response to the candidate categories of the object having been determined, a category of the object using second neural networks respectively corresponding to the candidate categories.

Abstract: An image search method and an apparatus may include acquiring an image to be searched; extracting a multi-scale feature of the image to be searched; determining a hash value of the image to be searched according to the multi-scale feature; and obtaining original images similar to the image to be searched by comparing the multi-scale feature of the image to be searched with a multi-scale feature of each original image corresponding to a hash bucket to which the hash value belongs.