Abstract: Embodiments of the present disclosure provide methods and apparatuses for obtaining sample images, and electronic devices. The method includes: acquiring images of stacked bodies, where the stacked bodies in different images have different item information, and the item information comprises: an attribute and a stacking mode of a stacked body; and taking an acquired image as a sample image when the acquired image meets an image quality condition.

Abstract: Embodiments of the present specification provide a target recognition method, apparatus, and system. The method comprises: obtaining an image recognition result and a radio frequency recognition result of target objects in a target region, and then determining the distribution of the target objects in the target region according to the radio frequency recognition result and the image recognition result. Since the radio frequency recognition result and the image recognition result are fused, the target objects in the target region can be accurately recognized, so as to improve the recognition accuracy.

Abstract: The method for vehicle appearance feature recognition includes: multiple region segmentation results of a target vehicle are obtained from an image to be recognized; global feature data and multiple pieces of region feature data are extracted from the image to be recognized based on the multiple region segmentation results; and the global feature data and the multiple pieces of region feature data are fused to obtain appearance feature data of the target vehicle.

Abstract: The method for vehicle appearance feature recognition includes: multiple region segmentation results of a target vehicle are obtained from an image to be recognized; global feature data and multiple pieces of region feature data are extracted from the image to be recognized based on the multiple region segmentation results; and the global feature data and the multiple pieces of region feature data are fused to obtain appearance feature data of the target vehicle.

Abstract: A method for image labeling can include the following operations. An image to be labeled and a first scale indicator are acquired. A pixel neighborhood is constructed based on the first person point under the condition that the first scale indicator is more than or equal to a first threshold, the pixel neighborhood including a second pixel different from the first person point. A position of the second pixel is determined as the person point tag of the first person.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

Abstract: Methods, devices, systems, and apparatus for target object identification are provided. In one aspect, a method includes: performing classification on a to-be-identified target object in a target image to determine a prediction category of the to-be-identified target object, determining whether the prediction category is correct according to a hidden layer feature for the to-be-identified target object, and outputting prompt information in response to the prediction category being incorrect.

Abstract: The present disclosure provides a method, apparatus and system for identifying target objects. The method includes: clipping out a target image from an acquired image, wherein the target image involves a plurality of target objects to be identified that are stacked; adjusting a height of the target image to a preset height, wherein a height direction of the target image corresponds to a direction in which the plurality of target objects are stacked; extracting a feature map of the adjusted target image; segmenting the feature map in a dimension corresponding to the height direction of the target image to obtain a preset number of segment features; and identifying the target objects based on each of the preset number of segment features.

Abstract: The method for vehicle appearance feature recognition includes: multiple region segmentation results of a target vehicle are obtained from an image to be recognized; global feature data and multiple pieces of region feature data are extracted from the image to be recognized based on the multiple region segmentation results; and the global feature data and the multiple pieces of region feature data are fused to obtain appearance feature data of the target vehicle.

Abstract: A speed measurement method and apparatus, an electronic device and a storage medium are provided. The method includes: a first image to be processed and a second image to be processed are acquired, each of the first image to be processed and the second image to be processed including a first target; a first position of the first target in the first image to be processed, a second position of the first target in the second image to be processed, and a first transmission parameter of a first moving distance are acquired; and a speed of the first target is obtained according to the first moving distance, the first transmission parameter and moving time, the moving time being obtained according to a timestamp of the first image to be processed and a timestamp of the second image to be processed.

Abstract: A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Abstract: The present disclosure relates to a network training method and device, an image processing method and device, the method comprising: performing pixel shuffling on a first image in a training set to obtain a second image, wherein the first image is an image subjected to pixel shuffling; performing, by a feature extraction network of a neural network, feature extraction on the first image to obtain a first image feature, and performing, by a feature extraction network, feature extraction on the second image to obtain a second image feature; performing, by a recognition network of the neural network, recognition on the first image feature to obtain a recognition result of the first image; and training the neural network according to the recognition result, the first image feature and the second image feature. Embodiments of the present disclosure enable improvement of recognition precision of neural networks.

Abstract: The present disclosure relates to a method and apparatus for association detection for a human face and a human hand, an electronic apparatus and a storage medium. The method comprises: acquiring a first image, the first image being an image of a human object; performing feature extraction on the first image to obtain a first feature map of multiple dimensions; performing a feature fusion processing on the first feature map of multiple dimensions to obtain a second feature map of multiple dimensions, the dimensions of the second feature map corresponding to the dimensions of the first feature map in a one-to-one manner; detecting, on the basis of the obtained second feature map of multiple dimensions, a human face position and a human hand position associated with the same human object in the first image. The embodiment of the present disclosure enables easy and convenient association detection for human face and human hand.

Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring a first image and a first attention map of a first target object in the first image, where the first attention map indicates the position of the first target object in the first image; acquiring a second image and a second mask image of the second image, where pixel values of pixels in the second mask image are equal; and obtaining, according to first input information and second input information, a first prediction result that indicates the position of the first target object in the second image, where the first input information is determined according to the first image and the first attention map, and the second input information is determined according to the second image and the second mask image.

Abstract: Image association methods, systems and apparatuses are provided. The method includes: obtaining a first image and a second image, where the first image is obtained by capturing a scene by a first image capture device at a first view, the second image is obtained by synchronously capturing the scene by a second image capture device at a second view, and the first view is different from the second view; determining an epipole of the first image capture device on a plane of the second image; determining a projection point of a first target point in a first bounding box on the second image, where the first bounding box is a bounding box of a target in the first image; and determining an association bounding box of the first bounding box in the second image according to the epipole and the projection point.

Abstract: The disclosure relates to a neural network searching method and device. The method comprises: acquiring a neural network library to be searched and a training data set; sorting neural networks with a number of trained cycles of a first preset value in the neural network library to be searched according to a descending order of recognition accuracy on the training data set to obtain a first neural network sequence set, and taking first M neural networks in the first neural network sequence set as a first neural network set to be trained; performing first-stage training on the first neural network set to be trained by using the training data set, wherein the number of training cycles of the first-stage training is a second preset value; and taking a neural network with a number of trained cycles of the sum of the first preset value and the second preset value in the neural network library to be searched as a target neural network. A corresponding device is also disclosed.

Abstract: The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring at least two target images; determining an attention map of at least one target in each of the at least two target images according to a result of detecting target of each target image, where the attention map indicates the position of one target in a target image; and determining, based on each target image and the attention map of the at least one target in the each target image, a result of association that indicates whether a correspondence exists between at least some of targets in different target images.

Abstract: Embodiments of the present disclosure disclose an image processing method and apparatus, and an electronic device. The method includes: obtaining a key frame image that includes a target object, and obtaining a to-be-processed frame image that includes the target object; extracting a feature point in the key frame image and a feature point in the to-be-processed frame image, respectively; determining a matching relationship between each feature point in the key frame image and each feature point in the to-be-processed frame image; determining a transformation relationship based on the feature point in the key frame image and a feature point in the to-be-processed frame image that matches the feature point in the key frame image; and processing the to-be-processed frame image based on the transformation relationship to obtain a target frame image.