Event Video Clip Generation Using Object Tracking and Reframing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for extracting specific clips from recorded events, such as sporting events, require significant manual effort and resources, as users need to manually review and edit video to identify relevant portions.
Innovation Solution
A device and application that automatically generates clips of interest by applying object detection models to video, tracking objects, and selecting an optimal sequence using a graph-based approach to create professional-looking highlight clips that can be quickly shared.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and editing of video is performed to identify relevant portions, then the accuracy of clip selection is improved, but the time and human resources required increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing video content, detecting objects, tracking their movements, and selecting relevant clips without requiring manual human intervention. The automated clip generation system processes the entire video to identify and extract meaningful segments, eliminating the need for users to manually review and edit the complete video file.
Solution Approach 2:
The patent replaces the mechanical manual editing process with an automated computational system. Instead of human operators visually reviewing video frames and manually selecting clips, the system uses computer vision algorithms, object detection models, and automated decision-making to perform the same function, thereby reducing time consumption while maintaining clip selection accuracy.
2Loss of information
If complete video is reviewed manually to find specific portions, then the completeness of content analysis is improved, but the operational complexity and resource consumption increase
Solution Approach 1:
The system extracts only the essential information and relevant clips from the complete video without requiring manual analysis of every frame. By using automated object detection and tracking algorithms, the system identifies and extracts meaningful segments based on detected objects, their movements, and contextual relevance, thereby maintaining content analysis completeness while significantly reducing operational complexity.
Solution Approach 2:
The patent introduces an intermediary automated processing layer between the raw video and the final clip selection. This intermediary system includes object detection models, tracking algorithms, and relevance assessment modules that automatically analyze video content and determine which segments are most relevant, eliminating the need for direct manual review of the complete video and reducing operational complexity.
3Productivity
If automated object detection and tracking is applied to generate clips, then the speed of clip generation is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the complex video processing task into multiple independent modules: object detection, object tracking, relevance assessment, and clip extraction. Each module handles a specific function and can be optimized independently, allowing the system to achieve high clip generation speed through parallel processing while managing overall system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing the video data to detect objects, track their movements, and identify potential clips of interest before final selection. This preliminary analysis enables the system to quickly generate clips at high speed by having the data already organized and ready for extraction, while the complexity is managed through efficient algorithm design and data structures.
Data Source
AI summary
A device obtains video of an event captured by an image capture device and detects one or more objects within frames of the video. Tracking data for detected objects across frames of the video is also generated for detected objects. Based on the tracking data, the device generates a graph representing detections of objects in different frames and selects an optimal path traversing the graph. The device selects a set of key frames based on nodes along the optimal path and applies one or more reframing methods to the set of key frames to generate a clip comprising a subset of the video. The clip may be distributed to user devices or to a backend server for distribution.