Abstract: A method and an apparatus for early warning of climbing behaviors, an electronic device, and a storage medium are disclosed. The method includes: acquiring video image data including a monitored target and at least one object (11); acquiring behavior information of the at least one object when it is determined that the at least one object enters a target area corresponding to the monitored target (12); marking video frames in which the at least one object is included when it is determined that the behavior information indicates that the at least one object climbs the monitored target (13). By marking the video frames in the video image data, the behavior of the object climbing the monitored target can be found in time, and the management efficiency can be improved.

Abstract: The present disclosure provides a traffic statistics collection method and apparatus. The method includes: obtaining a first image of a target place; performing body detection on the first image to obtain body detection boxes of respective target objects in the first image, where the body detection boxes indicate body areas of the respective target objects; for each target object of the respective target objects, determining a head detection box of the target object according to the body detection box of the target object, wherein the head detection box indicates a head area of the target object; and obtaining a traffic statistics result of the target place according to head detection boxes of the respective target objects.

Abstract: Provided is a method for training a target detection model. The method includes: determining a first region in a sample image, wherein the first region is a target region predicted by the target detection model in the sample image; determining a relationship between an intersection region and the first region, wherein the intersection region is an intersection of the first region and a second region, wherein the second region is a region annotated for a target in the sample image in a data annotation phase, and surrounds the target in the sample image; determining, in response to the relationship between the intersection region and the first region satisfying a target relationship, a predetermined low loss function value as a loss function value of the first region, wherein the predetermined low loss function value is a constant; and training the target detection model with reference to the loss function value.

Abstract: An action recognition method includes: an electronic device sampling a plurality of image frames of a video to be recognized, and according to the plurality of image frames and a pre-trained self-attention model, determining a probability distribution of the video to be recognized being similar to a plurality of action categories; further, based on the probability distribution of the video to be recognized being similar to the plurality of action categories, the electronic device determining, from the plurality of action categories, an action category having a probability greater than or equal to a preset threshold as a target action category corresponding to the video to be recognized.

Abstract: Disclosed is a method for training an image processing model. The method includes acquiring a plurality of original image sets; cropping the plurality of original images in the plurality of original image sets to acquire a training sample set, wherein each of the training samples represents one original image, or a subimage acquired by cropping one original image; determining a plurality of positive sample pairs and a plurality of negative sample pairs from the training sample set, wherein each of the positive sample pairs comprises two training samples acquired based on different said original images in the same original image set, and each of the negative sample pairs comprises two training samples acquired based on the original images in different said original image sets; and training the image processing model based on the plurality of positive sample pairs and the plurality of negative sample pairs.

Abstract: The present disclosure provides an image processing method, an apparatus, a computing device, and a medium, which relates to deep learning technology. In the present disclosure, after acquiring the to-be-processed target image, the first similarities between the target image and the first images of categories are determined based on the first feature vector corresponding to the target image and the second feature vectors respectively corresponding to the plurality of first images. Moreover, based on the first color distribution information of the target region in the target image and the second color distribution information of the target regions in the plurality of first images, the second similarities between the target image and the plurality first images are determined, so that the image category to which the target image belongs can be determined jointly based on the first similarities and the second similarities.

Abstract: A method and an apparatus for early warning of climbing behaviors, an electronic device, and a storage medium are disclosed. The method includes: acquiring video image data including a monitored target and at least one object (11); acquiring behavior information of the at least one object when it is determined that the at least one object enters a target area corresponding to the monitored target (12); marking video frames in which the at least one object is included when it is determined that the behavior information indicates that the at least one object climbs the monitored target (13). By marking the video frames in the video image data, the behavior of the object climbing the monitored target can be found in time, and the management efficiency can be improved.