Video Key Time Point Identification Using Shot Type Segmentation
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Solution Overview
Problem
Conventional image processing algorithms for determining key time points in sports match videos have poor robustness and adaptability, leading to low accuracy, especially in complex scenes and with camera movement.
Innovation Solution
A method using machine learning models, specifically an image classification model and an image detection model, to segment videos and detect key time points based on shot types and object locations, with preprocessing and smoothing techniques to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing algorithms are used to determine key time points, then the processing speed is fast, but the accuracy is low due to poor robustness and adaptability in complex scenes and camera movement
Solution Approach 1:
The video is segmented into multiple shot types (e.g., close-up, medium shot, long shot) based on camera movement and scene changes. Each shot type is processed separately by the image detection model, allowing the system to handle complex scenes more effectively by dividing the overall task into manageable segments with specific characteristics
Solution Approach 2:
The system changes the parameter of shot type classification to improve accuracy. By identifying and separating different shot types (close-up, medium shot, long shot) and applying different detection strategies for each, the system adapts to varying scene complexities and camera movements, thereby improving key time point recognition accuracy without uniformly increasing overall system complexity
2Measurement precision
If machine learning models are used to segment videos and detect key time points, then the accuracy is improved, but the processing time increases
Solution Approach 1:
The video processing is segmented into distinct stages: shot type classification first, then selective object detection only in relevant shot types. This segmentation allows the computationally intensive detection model to run only where necessary, reducing overall processing time while maintaining high accuracy
Solution Approach 2:
Instead of applying the image detection model to every frame uniformly, the system applies it partially only to frames belonging to specific shot types that are more likely to contain key moments. This partial action approach reduces unnecessary computational overhead while preserving detection accuracy for critical moments
3Productivity
If the image detection model processes all video frames uniformly, then the coverage is complete, but the efficiency decreases due to redundant processing
Solution Approach 1:
The video frames are segmented into different shot types first. The image detection model then processes only the segments (shot types) that are relevant for key moment detection, such as close-up shots or specific action sequences. This segmentation-based selective processing maintains detection coverage for important moments while eliminating redundant processing of irrelevant frames
Solution Approach 2:
Different processing qualities are applied to different local regions (shot types) of the video. High-detailed detection is applied locally to shot types that are more likely to contain key moments, while standard processing is applied to other shot types. This local quality differentiation improves processing efficiency without sacrificing detection coverage for critical regions
Data Source
AI summary
A method for recognizing a key time point in a video includes: obtaining at least one video segment by processing each image frame in the video by an image classification model; determining a target video segment in the at least one video segment based on a shot type; obtaining respective locations of a first object and a second object in an image frame of the target video segment by an image detection model; and based on a distance between the location of the first object and the location of the second object in the image frame satisfying a preset condition, determining a time point of the image frame as the key time point of the video.