Automatic Video Reframing via Neural Object Tracking
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Solution Overview
Problem
Conventional video editing software is inefficient and prone to temporal inconsistencies when performing reframing operations, as it relies heavily on user input to identify and track objects across multiple video frames, leading to time-consuming and inaccurate results.
Innovation Solution
A video editing system that uses a neural network to determine object attributes such as hotspots, masks, and trajectories, allowing for automatic reframing parameters to be generated and applied, ensuring temporally consistent reframing effects through a reframing engine that processes videos using segmentation and hotspot modules, crop suggestion modules, and user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video editing software relies on user input to identify and track objects across multiple video frames, then the system maintains simplicity in design, but the editing process becomes time-consuming and prone to temporal inconsistencies
Solution Approach 1:
The patent replaces manual user input mechanisms with an automated neural network-based object detection and tracking system. The neural network automatically identifies objects across video frames and generates reframing parameters, eliminating the need for users to manually track objects and reducing temporal inconsistencies in the reframing process
Solution Approach 2:
The system enables self-service by allowing the video editing system to automatically perform object identification, tracking, and reframing parameter generation without requiring continuous user intervention. The neural network processes video frames autonomously to produce consistent reframing effects across the entire video sequence
2Measurement precision
If manual object tracking is used across multiple video frames, then the system requires minimal computational resources, but the accuracy of object tracking and reframing consistency deteriorates
Solution Approach 1:
The neural network performs preliminary object detection and attribute extraction on individual video frames before the reframing process. By pre-identifying objects and their attributes (bounding boxes, masks, hotspots) in advance, the system achieves high tracking accuracy while optimizing computational resource usage during the actual reframing operation
Solution Approach 2:
The system segments the complex task of video reframing into distinct components: object detection, attribute extraction (bounding boxes, masks, hotspots), trajectory tracking, and reframing parameter generation. This segmentation allows each component to be optimized independently, improving overall accuracy while managing computational resources efficiently
3Reliability
If automated neural network processing is implemented for object detection and tracking, then reframing efficiency and consistency improve, but the computational complexity and processing time increase
Solution Approach 1:
The system applies partial processing by focusing the neural network's attention on key object attributes (bounding boxes, masks, hotspots) rather than analyzing every pixel in detail. This selective approach maintains high reframing consistency while reducing overall processing time by extracting only the essential information needed for accurate reframing
Data Source
AI summary
Systems and methods provide reframing operations in a smart editing system that may generate a focal point within a mask of an object for each frame of a video segment and perform editing effects on the frames of the video segment to quickly provide users with natural video editing effects. A reframing engine may processes video clips using a segmentation and hotspot module to determine a salient region of an object, generate a mask of the object, and track the trajectory of an object in the video clips. The reframing engine may then receive reframing parameters from a crop suggestion module and a user interface. Based on the determined trajectory of an object in a video clip and reframing parameters, the reframing engine may use reframing logic to produce temporally consistent reframing effects relative to an object for the video clip.