Abstract: A human behavior recognition method, a device, and a storage medium are provided, which are related to the field of artificial intelligence, specifically to computer vision and deep learning technologies, and applicable to smart city scenarios. The method includes: obtaining attribute information of a target object and N pieces of candidate behavior-related information of a target human from a target image, wherein N is an integer greater than or equal to 1; determining target behavior-related information based on comparison results between the N pieces of candidate behavior-related information and the attribute information of the target object; and determining a behavior recognition result of the target human based on the target behavior-related information.

Abstract: Provided are an image detection method and apparatus, a device, a storage medium and a computer program product, the method includes: performing feature extraction on an image to be detected to obtain a feature map; performing target detection on the feature map through a target branch network to obtain target detection information of candidate anchor boxes at a pixel position of the feature map; performing keypoint detection on the feature map through a keypoint branch network to obtain positions of keypoints in the candidate anchor boxes at the pixel position of the feature map, and generating heatmap groups of the candidate anchor boxes according to the positions of the keypoints; and obtaining information about a target object in the image to be detected and positions of keypoints of the target object according to the target detection information and the heatmap groups.

Abstract: Provided are a joint perception model training method, a joint perception method, a device, and a storage medium. The joint perception model training method includes: acquiring sample images and perception tags of the sample images; acquiring a preset joint perception model, where the joint perception model includes a feature extraction network and a joint perception network; performing feature extraction on the sample images through the feature extraction network to obtain target sample features; performing joint perception through the joint perception network according to the target sample features to obtain perception prediction results; and training the preset joint perception model according to the perception prediction results and the perception tags, where the joint perception includes executing at least two perception tasks.

Abstract: An image processing and model training methods, an electronic device, and a storage medium are provided, and relate to the technical field of artificial intelligence, and in particular to the technical fields of computer vision and deep learning, which can be specifically applied to smart cities and intelligent cloud scenes. The image processing method includes: obtaining at least one first feature map of an image to be processed, wherein feature data of a target pixel in the first feature map is generated according to the target pixel and another pixel within a set range around the target pixel; determining a classification to which the target pixel belongs according to the feature data of the target pixel; and determining a target object corresponding to the target pixel and association information of the target object according to the classification to which the target pixel belongs.

Abstract: The present disclosure provides an object tracking method, an object tracking device, an electronic device and a computer-readable storage medium, and relates to the field of computer vision technology.

Abstract: A method and apparatus for detecting an object. The method includes: inputting a to-be-detected picture into a target detection model, marking at least one region of interest in the picture using the target detection model, and determining an initial confidence that each region of interest contains a preset target object; determining concentration information of an interferent in the picture; and determining, based on the concentration information and the initial confidence corresponding to the region of interest, a target confidence that the region of interest contains the preset target object.

Abstract: A method and apparatus for tracking an object, an electronic device, and a readable storage medium are provided. The method can include: determining an object re-identification feature of each target object in a target frame image, the object re-identification feature comprising position information of each target object; and performing object tracking based on the object re-identification feature of each target object.

Abstract: The present disclosure provides a target tracking method, a target tracking device and an electronic apparatus. The target tracking method includes: inputting an ith image and an (i?1)th image in a to-be-detected video stream into a target deep learning model, i being an integer greater than 1; detecting a target in the ith image to obtain a first target detection box, and tracking the target in the (i?1)th image to obtain a tracking heatmap; and determining a target tracking result in accordance with the first target detection box, the tracking heatmap and the (i?1)th image.

Abstract: A human behavior recognition method, a device, and a storage medium are provided, which are related to the field of artificial intelligence, specifically to computer vision and deep learning technologies, and applicable to smart city scenarios. The method includes: obtaining attribute information of a target object and N pieces of candidate behavior-related information of a target human from a target image, wherein N is an integer greater than or equal to 1; determining target behavior-related information based on comparison results between the N pieces of candidate behavior-related information and the attribute information of the target object; and determining a behavior recognition result of the target human based on the target behavior-related information.

Abstract: A method and apparatus for tracking a target are provided. The method may include: generating a position of a candidate box of a to-be-tracked target in a to-be-processed image; determining, for a pixel in the to-be-processed image, a probability that each anchor box of at least one anchor box arranged for the pixel includes the to-be-tracked target, and determining a deviation of the candidate box corresponding to the anchor box relative to the anchor box; determining candidate positions of the to-be-tracked target corresponding to the at least two anchor boxes respectively; and combining at least two candidate positions among the determined candidate positions to obtain a position of the to-be-tracked target in the to-be-processed image.

Abstract: Provided are an image detection method and apparatus, a device, a storage medium and a computer program product, the method includes: performing feature extraction on an image to be detected to obtain a feature map; performing target detection on the feature map through a target branch network to obtain target detection information of candidate anchor boxes at a pixel position of the feature map; performing keypoint detection on the feature map through a keypoint branch network to obtain positions of keypoints in the candidate anchor boxes at the pixel position of the feature map, and generating heatmap groups of the candidate anchor boxes according to the positions of the keypoints; and obtaining information about a target object in the image to be detected and positions of keypoints of the target object according to the target detection information and the heatmap groups.