Gaze Vector Intersection for Panoramic Video Area of Interest
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video editing technologies struggle to automatically identify and summarize interesting portions of video data, often omitting engaging content like stages or aquariums while retaining less interesting elements like faces, especially in environments such as concert venues or aquariums.
Innovation Solution
A system that tracks where people are looking using gaze vectors and determines interesting areas by intersecting these vectors, generating heat maps and video tags to automatically summarize video data by highlighting areas of persistent or instantaneous interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic video summarization is performed using existing technologies, then video editing efficiency is improved, but interesting content such as stages and aquariums is omitted while retaining less interesting elements like faces
Solution Approach 1:
The system uses gaze tracking data as feedback to continuously adjust and refine video summarization. By monitoring where viewers look during video playback, the system identifies interesting regions and uses this feedback to automatically generate summaries that prioritize these regions, thereby retaining interesting content while improving editing efficiency
Solution Approach 2:
The video summarization system serves itself by automatically identifying interesting content through gaze data analysis without requiring manual intervention. The system autonomously determines which regions to include in summaries based on accumulated gaze information, enabling self-service video editing that maintains content quality while improving productivity
2Measurement precision
If gaze tracking is used to determine areas of interest, then video summarization accuracy is improved, but processing complexity increases
Solution Approach 1:
The system segments the video content into multiple regions of interest based on gaze data, rather than treating the entire video as a single unit. By dividing the video into manageable segments and analyzing gaze patterns within each segment, the system achieves high detection accuracy while keeping processing complexity manageable through localized analysis
Solution Approach 2:
The system adds a temporal dimension to gaze data analysis by tracking gaze patterns over time and generating heat maps that show cumulative interest in different regions. This dimensional transformation allows the system to identify persistent areas of interest more accurately while using efficient algorithms to process the extended data
3Loss of information
If heat maps and gaze vectors are processed to identify persistent and instantaneous areas of interest, then video content relevance is improved, but computational requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources only on regions where gaze data indicates interest. Rather than uniformly processing the entire video, the system concentrates computational energy on identified areas of interest, achieving high content relevance while reducing overall computational requirements through selective processing
Solution Approach 2:
The system performs preliminary processing of gaze data to generate heat maps and identify potential areas of interest before conducting detailed video analysis. This preliminary action filters out irrelevant regions early in the process, allowing subsequent computational steps to focus only on promising candidates and thereby reducing total energy consumption
Data Source
AI summary
A system and method for identifying an interesting area within video data using gaze tracking is provided. The system determines gaze vectors for individual faces and determines if the gazes are aligned on a single object. For example, the system may map the gaze vectors to a three-dimensional space and determine a point at which the gaze vectors converge. The point may be mapped to the video data to determine the interesting area having a high priority. The system may generate a heat map indicating gaze tracking over time to delineate persistent interesting areas and instantaneous interesting areas.