Real-Time Video Frame Adjustment Using AI Activity Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video processing technologies lack the ability to apply dynamic effects in real-time on video streams, restricting creativity and realism in video content creation, particularly for influencers and content creators on digital platforms.
Innovation Solution
A method and apparatus for real-time frame adjustment on video streams using artificial intelligence (AI) to identify activities, prioritize key focus areas, and apply dynamic effects based on tracking and activity categories, enabling seamless integration of dynamic effects into live video streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic effects are applied only on recorded videos through manual post processing, then video editing capabilities are enhanced, but real-time processing capability is lost and time consumption increases
Solution Approach 1:
The system performs activity classification and focus area identification in advance on incoming video frames, preparing the processing pipeline before the actual effect application. This preliminary analysis enables rapid effect application without manual intervention, resolving the contradiction between enhanced editing capability and time consumption.
Solution Approach 2:
The system automatically detects activities, identifies focus areas, and applies dynamic effects without requiring manual post-processing intervention. The automated pipeline handles the entire workflow from raw video to enhanced output, eliminating time-consuming manual editing while maintaining versatile effect application.
2Manufacturing precision
If manual post processing is used to add dynamic effects, then video quality is improved, but ease of operation deteriorates and complexity increases
Solution Approach 1:
The system performs automatic activity classification, focus area detection, and effect application without requiring user intervention. The automated pipeline maintains high video quality through AI-based processing while dramatically improving ease of operation by eliminating manual editing steps.
Solution Approach 2:
The system dynamically adjusts processing parameters based on detected activity types and focus areas. By automatically modifying parameters such as effect intensity, focus area coordinates, and processing speed, the system maintains optimal video quality while simplifying operation to a single button press.
3Productivity
If real-time frame adjustment is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system divides the complex real-time processing task into separate modular stages: activity classification, focus area identification, and effect application. Each stage operates independently on specific data, enabling parallel processing and maintaining high productivity while reducing overall system complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary processing layer that analyzes video frames and generates control signals for effect application. This intermediary layer simplifies the interaction between the camera and effect rendering systems, enabling real-time processing without directly increasing the complexity of individual components.
4Adaptability or versatility
If dynamic effects are applied based on activity detection, then adaptability is improved, but measurement precision requirements increase
Solution Approach 1:
The system adjusts detection sensitivity and classification thresholds based on the specific activity being detected. By dynamically changing detection parameters according to context, the system achieves high adaptability to different activities while maintaining sufficient measurement precision through adaptive thresholding and multi-scale analysis.
Data Source
AI summary
A method for real-time frame adjustment on a video stream, includes: obtaining, in real-time, one or more frames of the video stream; identifying one or more activities from the one or more frames; prioritizing one or more key focus areas from a set of key focus areas in the one or more activities; determining at least one of one or more target actions and one or more target effects, based on at least one of: a tracking of the one or more prioritized key focus areas, the set of key focus areas, and one or more activity categories of the one or more activities; and applying, in real-time, at least one of the one or more target effects and the one or more target actions to the one or more frames of the video stream.