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.

This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

This disclosure relates to an information pushing method in a vehicle driving scene. The method may include receiving push information in the vehicle driving scene and obtaining driving scene image information collected by an in-vehicle image collection device. The method may further include identifying scene category identification information based on the driving scene image information. The scene category identification information is for indicating a category of the environmental information. The method may further include pushing, in response to the scene category identification information satisfying a push condition, the push information in the vehicle driving scene.

A method for processing video data, comprising: receiving raw video data, representative of a plurality of frames; detecting, using the raw video data, one or more regions of interest in a detection frame that belongs to the plurality of frames, for example using a region proposal network; performing a cropping process on a portion of the raw video data representative of the detection frame, based on the regions of interest, so as to generate cropped raw video data; performing image processing on the cropped raw video data, including demosaicing, so as to generate processed image data for the detection frame; and analyzing the processed image data, for example using an object detection process, to determine information relating to at least one of said one or more regions of interest.

Techniques are disclosed for determining whether to include a bodyprint in a cluster of bodyprints associated with a recognized person. For example, a device performs facial recognition to identify the identity of a first person. The device also identifies and stores physical characteristic information of the first person, the stored information associated with the identity of the first person based on the recognized face. Subsequently, the device receives a second video feed showing an image of a second person whose face is also determined to be recognized by the device. The device then generates a quality score for physical characteristics in the image of the user. The device can then add the image with the physical characteristics to a cluster of images associated with the person if the quality score is above a threshold, or discard the image if not.

One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

An image processing apparatus comprises a detection unit configured to detect a specific portion of a subject before detecting the subject in a search region of a first frame image, and a processing unit configured to perform tracking processing to track the subject in the first frame image after the detection unit has detected the specific portion.

A method of training a convolutional neural network model to predict diabetes from an image of a retina is provided. The method of training a convolutional neural network includes processing a first dataset, wherein processing the first dataset comprises: extracting a circular region from a retinal image, resizing the circular region, cropping the circular region, and placing the circular region onto a black background; training an initial model using a second dataset to yield a first model; training the first model using a third dataset to yield a second model; and training the second model using the first dataset to yield a third model.

A method, performed by an electronic device, of generating a three-dimensional (3D) image, includes: obtaining a first image through a first camera of the electronic device and obtaining a second image through a second camera of the electronic device; obtaining depth information of a pixel included in the first image; identifying, based on the depth information, a first layer image and a second layer image from the first image; inpainting, based on the first image and the second image, at least a part of the first layer image; and generating, based on the second layer image and the inpainted first layer image, the 3D image including a plurality of layers.