Abstract: A method of recognizing illegal parking of a vehicle, a device, and a storage medium, which relate to the field of artificial intelligence, and in particular to the fields of deep learning, cloud computing, computer vision, etc. The method includes: obtaining a video image collected by an electronic device; recognizing a parking area of the vehicle in the video image; determining a shooting angle used by the electronic device for collecting the video image; determining an illegal parking area in the video image based on the shooting angle; and recognizing whether the vehicle is illegally parked or not based on the parking area of the vehicle and the illegal parking area.

Abstract: A method for detecting a surface defect, a method for training model, an apparatus, a device, and a medium, are provided. The method includes: inputting a surface image of the article for detection into a defect detection model to perform a defect detection, and acquiring a defect detection result output by the defect detection model; inputting a surface image of a defective article determined to be defective into an image discrimination model based on the defect detection result to determine whether the surface image of the defective article is defective, wherein the image discrimination model is a trained generative adversarial networks model, and the generative adversarial networks model is obtained by training using a surface image of a defect-free good article; and adjusting the defect detection result of the surface image of the defective article according to a determination result of the image discrimination model.

Abstract: The present application discloses a vehicle re-identification method and apparatus, a device and a storage medium, which relates to the field of computer vision, intelligent search, deep learning and intelligent transportation. The specific implementation scheme is: receiving a re-identification request from a terminal device, the re-identification request including a first image of a first vehicle shot by a first camera and information of the first camera; acquiring a first feature of the first vehicle and a first head orientation of the first vehicle according to the first image; determining a second image of the first vehicle from images of multiple vehicles according to the first feature, multiple second features extracted based on the images of the multiple vehicles in an image database, the first head orientation of the first vehicle, and the information of the first camera; and transmitting the second image to the terminal device.

Abstract: A method for tracking vehicles includes: extracting a target image at a current moment from a video stream obtained during traveling of vehicles; performing instance segmentation on the target image to obtain detection boxes corresponding to individual vehicles in the target image; extracting, from the detection box for each vehicle, a set of pixel points corresponding to each vehicle; processing image features of each pixel point in the set of pixel points corresponding to each vehicle to determine features of each vehicle in the target image; and determining, according to the features of each vehicle in the target image and the degree of matching between the features of each vehicle in past images, movement trajectory of each vehicle in the target image, wherein the past images are n images adjacent to and before the target image in the video stream, and n is a positive integer.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for identifying a video. A specific embodiment of the method includes: acquiring a predetermined number of video frames from a video to be identified to obtain a video frame sequence; performing the following processing step: importing the video frame sequence into a pre-trained video identification model to obtain a classification tag probability corresponding to the video frame sequence, wherein the classification tag probability is used to characterize a probability of identifying a corresponding tag category of the video to be identified; and setting, in response to the classification tag probability being greater than or equal to a preset identification accuracy threshold, a video tag for the video to be identified according to the classification tag probability, or else increasing the number of video frames in the video frame sequence and continuing to perform the above processing step.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for searching a video segment, a device and a medium, and relate to the field of video data search. The method includes: sampling video frames from a target video and videos to be searched in a video library, and extracting features from the sampled frames; matching the target video and the videos to be searched according to the extracted features to determine a candidate video to be searched that matches the target video; determining at least one candidate video segment from the determined candidate video, and calculating a degree of matching between the target video and each candidate video segment based on the extracted features of each sampled frame; and determining a video segment matching the target video in the videos to be searched according to the calculated degree of matching between the target video and each candidate video segment.

Abstract: A method includes screening, by a video-clip screening module in a video description model, a plurality of video proposal clips acquired from a video to be analyzed, to acquire a plurality of video clips suitable for description. The plural video proposal clips acquired from the video to be analyzed may be screened by the video-clip screening module to acquire the plural video clips suitable for description; and then, each video clip is described by a video-clip describing module, thus avoiding description of all the video proposal clips, only describing the screened video clips which have strong correlation with the video and are suitable for description, removing the interference of the description of the video clips which are not suitable for description in the description of the video, guaranteeing the accuracy of the final descriptions of the video clips, and improving the quality of the descriptions of the video clips.

Abstract: The present application discloses a method and an apparatus for detecting wearing of a safety helmet, a device and a storage medium. The method for detecting wearing of a safety helmet includes: acquiring a first image collected by a camera device, where the first image includes at least one human body image; determining the at least one human body image and at least one head image in the first image; determining a human body image corresponding to each head image in the at least one human body image according to an area where the at least one human body image is located and an area where the at least one head image is located; and processing the human body image corresponding to the at least one head image according to a type of the at least one head image.

Abstract: A method and apparatus for positioning a key point, a device, and a storage medium are provided. The method may include: extracting a first feature map and a second feature map of a to-be-positioned image, the first feature map and the second feature map being different feature maps; determining, based on the first feature map, an initial position of a key point in the to-be-positioned image; determining, based on the second feature map, an offset of the key point; and adding the initial position of the key point with the offset of the key point to obtain a final position of the key point.

Abstract: A method and apparatus for detecting a temporal action of a video, an electronic device and a storage medium are disclosed, which relates to the field of video processing technologies. An implementation includes: acquiring an initial temporal feature sequence of a video to be detected; acquiring, by a pre-trained video-temporal-action detecting module, implicit features and explicit features of a plurality of configured temporal anchor boxes based on the initial temporal feature sequence; and acquiring, by the video-temporal-action detecting module, the starting position and the ending position of a video clip containing a specified action, the category of the specified action and the probability that the specified action belongs to the category from the plural temporal anchor boxes according to the explicit features and the implicit features of the plural temporal anchor boxes.

Abstract: A method and apparatus for vehicle damage assessment, an electronic device, and a computer-readable storage medium are provided. The method may include: extracting, from an input image, a first feature characterizing a part of a vehicle and a second feature characterizing a damage type of the vehicle; integrating the first feature and the second feature to generate a third feature characterizing a corresponding relation between the part and the damage type; converting the third feature into a characteristic vector; and determining a damage recognition result based on the characteristic vector. According to the technical solution of the disclosure, users can rapidly and accurately learn about the damage condition of the vehicle by providing pictures or videos of the damaged vehicle, thus providing an objective basis for subsequent damage assessment, claim settlement, and repair.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for processing an image. A specific embodiment of the method includes: acquiring a feature map of a target image, where the target image contains a target object; determining a local feature map of a target size in the feature map; combining features of different channels in the local feature map to obtain a local texture feature map; and obtaining location information of the target object based on the local texture feature map.

Abstract: Embodiments of the present disclosure provide a method and apparatus for generating an image. The method may include: receiving a first image including a face input by a user in an interactive scene; presenting the first image to the user; inputting the first image into a pre-trained generative adversarial network in a backend to obtain a second image output by the generative adversarial network; where the generative adversarial network uses face attribute information generated based on the input image as a constraint; and presenting the second image to the user in response to obtaining the second image output by the generative adversarial network in the backend.

Abstract: The present application discloses a video-based human behavior recognition method, apparatus, device and storage medium, and relates to the technical field of human recognitions. The specific implementation scheme lies in: acquiring a human rectangle of each video frame of the video to be recognized, where each human rectangle includes a plurality of human key points, and each of the human key points has a key point feature; constructing a feature matrix according to the human rectangle of the each video frame; convolving the feature matrix with respect to a video frame quantity dimension to obtain a first convolution result and convolving the feature matrix with respect to a key point quantity dimension to obtain a second convolution result; inputting the first convolution result and the second convolution result into a preset classification model to obtain a human behavior category of the video to be recognized.

Abstract: A method, an electronic device and a storage medium are provided. The method may include: acquiring a to-be-inspected image; inputting the to-be-inspected image into a pre-established vehicle detection model to obtain a vehicle detection result, where the vehicle detection result includes category information, coordinate information, coordinate reliabilities, and coordinate error information of detection boxes, and the vehicle detection model is configured for characterizing a corresponding relationship between images and vehicle detection results; selecting, based on the coordinate reliabilities of the detection boxes, a detection box from the vehicle detection result for use as a to-be-processed detection box; and generating, based on coordinate information and coordinate error information of the to-be-processed detection box, coordinate information of a processed detection box.

Abstract: A method and device for detecting a vehicle pose, relating to the fields of computer vision and automatic driving. The specific implementation solution comprises: inputting a vehicle left view point image and a vehicle right view point image into a part prediction and mask segmentation network model, and determining foreground pixel points and part coordinates thereof in a reference image; converting coordinates of the foreground pixels in the reference image into coordinates of the foreground pixels in a camera coordinate system so as to obtain a pseudo-point cloud, and fusing part coordinate of the foreground pixels and the pseudo-point cloud to obtain fused pseudo-point cloud; and inputting the fused pseudo-point cloud into a pre-trained pose prediction model to obtain a pose information of the vehicle to be detected.

Abstract: Embodiments of the present disclosure provide a video processing method, a video processing device and a related non-transitory computer readable storage medium. The method includes the following. Frame sequence data of a low-resolution video to be converted is obtained. Pixel tensors of each frame in the frame sequence data are inputted into a pre-trained neural network model to obtain high-resolution video frame sequence data corresponding to the video to be converted output by the neural network model. The neural network model obtains the high-resolution video frame sequence data based on high-order pixel information of each frame in the frame sequence data.

Abstract: A method of recognizing illegal parking of a vehicle, a device, and a storage medium, which relate to the field of artificial intelligence, and in particular to the fields of deep learning, cloud computing, computer vision, etc. The method includes: obtaining a video image collected by an electronic device; recognizing a parking area of the vehicle in the video image; determining a shooting angle used by the electronic device for collecting the video image; determining an illegal parking area in the video image based on the shooting angle; and recognizing whether the vehicle is illegally parked or not based on the parking area of the vehicle and the illegal parking area.

Abstract: A method and an apparatus for grounding a target video clip in a video are provided. The method includes: determining a current video clip in the video based on a current position; acquiring descriptive information indicative of a pre-generated target video clip descriptive feature, and executing a target video clip determining step which includes: determining current state information of the current video clip, wherein the current state information includes information indicative of a feature of the current video clip; generating a current action policy based on the descriptive information and the current state information, the current action policy being indicative of a position change of the current video clip in the video; the method further comprises: in response to reaching a preset condition, using a video clip resulting from executing the current action policy on the current video clip as the target video clip.

Abstract: The present disclosure provides a pedestrian re-identification method and apparatus, computer device and readable medium. The method comprises: collecting a target image and a to-be-identified image including a pedestrian image; obtaining a feature expression of the target image and a feature expression of the to-be-identified image respectively, based on a pre-trained feature extraction model; wherein the feature extraction model is obtained by training based on a self-attention feature of a base image as well as a co-attention feature of the base image relative to a reference image; identifying whether a pedestrian in the to-be-identified image is the same pedestrian as that in the target image according to the feature expression of the target image and the feature expression of the to-be-identified image.