Abstract: A method for adding animation to a video includes: detecting whether the hand of a user in the current video frame, which is currently captured, holds an object; if so, determining a target animation corresponding to the object; and determining an animation addition area, which corresponds to the target animation, on the current video frame, and adding the target animation to the animation addition area. Therefore, a corresponding animation special effect is added for an object held by a user in a video, such that the interaction between the user and the handheld object is visually reflected, thereby improving the interestingness of video filming, and providing technical support for improving the viscosity of the user with respect to a video product.

Abstract: Provided are a processing method and apparatus, an electronic device and a medium. The method includes steps described below. A target video is acquired; video information of the target video is determined; copy information corresponding to the video information is generated by using a video description model, where the copy information is generated by using the video description model and based on a task prompt and a copy keyword. Through this method, the video description model is constrained based on the task prompt and the copy keyword, so that the copy information of the target video is generated more accurately, and the coupling degree between the copy information and the target video is improved.

Abstract: Provided are a processing method and apparatus, an electronic device and a medium. The method includes steps described below. A target video is acquired; video information of the target video is determined; copy information corresponding to the video information is generated by using a video description model, where the copy information is generated by using the video description model and based on a task prompt and a copy keyword. Through this method, the video description model is constrained based on the task prompt and the copy keyword, so that the copy information of the target video is generated more accurately, and the coupling degree between the copy information and the target video is improved.

Abstract: Provided are a processing method, a terminal device, and a medium. The method includes the steps described below. Basic information of a target video is determined; attribute information of the target video is determined based on the basic information; in a case where the attribute information indicates that the target video is a video capable of being structured, chapter division is performed on the target video based on the basic information to obtain at least two video clips; and chapter description information of the at least two video clips, a key frame of the at least two video clips and video description information of the target video are determined.

Abstract: Provided are a video processing method and apparatus, an electronic device, and a medium. The method includes that a candidate slice information set of an original video is determined based on video information of the original video, the original video is segmented into a plurality of video segments based on each piece of candidate slice information in the candidate slice information set, the plurality of video segments is input into an evaluation model to determine a piece of probability information of each piece of candidate slice information, and target slice information of the original video is determined based on the piece of probability information corresponding to each piece of candidate slice information.

Abstract: A target video and video description information corresponding to the target video are acquired; salient object information of the target video is determined; a key frame category of the video description information is determined; and the target video, the video description information, the salient object information and the key frame category are input into a processing model to obtain a timestamp of an image corresponding to the video description information in the target video.

Abstract: Systems and method directed to performing video object segmentation are provided. In examples, video data representing a sequence of image frames and video data representing an object mask may be received at a video object segmentation server. Image features may be generated based on a first image frame of the sequence of image frames, image features may be generated based on a second image frame of the sequence of image frames; and object features may be generated based on the object mask. A transform matrix may be computed based on the image features of the first image frame and image features of the second image frame; the transform matrix may be applied to the object features resulting in transformed object features. A predicted object mask associated with the second image frame may be obtained by decoding the transformed object features.

Abstract: Systems and method directed to performing video object segmentation are provided. In examples, video data representing a sequence of image frames and video data representing an object mask may be received at a video object segmentation server. Image features may be generated based on a first image frame of the sequence of image frames, image features may be generated based on a second image frame of the sequence of image frames; and object features may be generated based on the object mask. A transform matrix may be computed based on the image features of the first image frame and image features of the second image frame; the transform matrix may be applied to the object features resulting in transformed object features. A predicted object mask associated with the second image frame may be obtained by decoding the transformed object features.

Abstract: A method and a system for counting pigs in a pig house. The system includes a computing device and an imaging device. The computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: receive images captured from one end to the other end of the house; detect keypoints in the images using a neural network; identify pig skeletons by associating several related keypoints; track the skeletons in the images to obtain trajectories; divide each image into an activated zone and a deactivated zone; designate a spatial value of 0 for the skeletons in the activated zone and a spatial value of 1 for the skeletons in the deactivated zone; summating first order difference of the spatial values for each trajectory to obtain a trajectory count; and add the trajectory counts to obtain pig count.

Abstract: A method and a system for self-checkout. The system includes a scanner, an imaging device, and a computing device. The computing device is configured to: initiate a self-checkout event; instruct the imaging device to capture video frames of a region of interest (ROI); track the product; record scanning status and location status of the product; in response to receive a scanning signal from the scanner, record scanning status of the product as scanned; calculate a shoplifting risk score based on a number of the product having the scanning status of unscanned and disappears from the table region or ROI; and provide a shoplifting warning when the shoplifting score is large.

Abstract: A method and a system for counting pigs in a pig house. The system includes a computing device and an imaging device. The computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: receive images captured from one end to the other end of the house; detect keypoints in the images using a neural network; identify pig skeletons by associating several related keypoints; track the skeletons in the images to obtain trajectories; divide each image into an activated zone and a deactivated zone; designate a spatial value of 0 for the skeletons in the activated zone and a spatial value of 1 for the skeletons in the deactivated zone; summating first order difference of the spatial values for each trajectory to obtain a trajectory count; and add the trajectory counts to obtain pig count.

Abstract: A method and a system for self-checkout. The system includes a scanner, an imaging device, and a computing device. The computing device is configured to: initiate a self-checkout event; instruct the imaging device to capture video frames of a region of interest (ROI); track the product; record scanning status and location status of the product; in response to receive a scanning signal from the scanner, record scanning status of the product as scanned; calculate a shoplifting risk score based on a number of the product having the scanning status of unscanned and disappears from the table region or ROI; and provide a shoplifting warning when the shoplifting score is large.

Abstract: A system includes a borescope and at least one processor. The borescope includes a camera configured to acquire an acquisition series of frames of at least one target component. The at least one processor is operably coupled to the camera, and is configured to acquire the acquisition series of frames from the camera; determine a blurriness metric value for each of the frames; select frames that satisfy a threshold for the blurriness metric value to form an inspection series of frames; and perform an inspection analysis for the at least one target component using the inspection series of frames.

Abstract: A system includes one or more processors configured to detect perimeter edges of one or more rotor blades of a turbine assembly as depicted in a series of image frames using boundary analysis performed on the image frames. The one or more processors are configured to identify a set of the image frames as key frames based on positional offsets between the perimeter edges that are detected in the image frames and a reference blade pose such that the key frames are able to be inspected for objects-of-interest without inspecting the image frames that are not the key frames.

Abstract: A system includes a borescope and at least one processor. The borescope includes a camera configured to acquire an acquisition series of frames of at least one target component. The at least one processor is operably coupled to the camera, and is configured to acquire the acquisition series of frames from the camera; determine a blurriness metric value for each of the frames; select frames that satisfy a threshold for the blurriness metric value to form an inspection series of frames; and perform an inspection analysis for the at least one target component using the inspection series of frames.

Abstract: A system includes one or more processors configured to detect perimeter edges of one or more rotor blades of a turbine assembly as depicted in a series of image frames using boundary analysis performed on the image frames. The one or more processors are configured to identify a set of the image frames as key frames based on positional offsets between the perimeter edges that are detected in the image frames and a reference blade pose such that the key frames are able to be inspected for objects-of-interest without inspecting the image frames that are not the key frames.