Method and device for identifying key time point of video, computer apparatus and storage medium

An apparatus for recognizing a key time point in a video, comprising:

• at least one memory storing computer program code;
• and
• at least one processor configured to access the at least one memory and operate as instructed by the computer program code, the computer program code comprising:
• first processing code configured to cause the at least one processor to obtain at least one video segment by processing each frame in the video by an image classification model, the image classification model being a machine learning model obtained by training according to a first sample image frame marked with a shot type, wherein each of the at least one video segment comprises at least two consecutive image frames in the video and each of the at least one video segment corresponds to one shot type among a plurality of shot types;
• second processing code configured to cause the at least one processor to determine a target video segment in the at least one video segment based on the shot type of the at least one video segment and obtain a first location of a first object and a second location of the second object in an image frame of the target video segment by an image detection model, the image detection model being a machine learning model obtained by training according to a second sample image frame marked with the first location of the first object and the second location of the second object;
• and
• determining code configured to cause the at least one processor to, based on a distance between the first location of the first object and the second location of the second object in the image frame satisfying a preset condition, determine a time point of the image frame as the key time point of the video.

A non-transitory computer-readable storage medium storing at least one computer program code configured to cause a computer processor to:

• obtain at least one video segment by processing each frame in a video by an image classification model, the image classification model being a machine learning model obtained by training according to a first sample image frame marked with a shot type, wherein each of the at least one video segment comprises at least two consecutive image frames in the video and each of the at least one video segment corresponds to one shot type among a plurality of shot types;
• determine a target video segment in the at least one video segment based on the shot type of the at least one video segment;
• obtain a first location of a first object and a second location of a second object in an image frame of the target video segment by an image detection model, the image detection model being a machine learning model obtained by training according to a second sample image frame marked with the first location of the first object and the second location of the second object;
• and
• based on a distance between the first location of the first object and the second location of the second object in the image frame satisfying a preset condition, determine a time point of the image frame as a key time point of the video.