Anchor Segment Detection for AR Tracking Under Subtitle Occlusion
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Solution Overview
Problem
Challenges exist in identifying static image segments suitable for augmented reality (AR) effects due to dynamic changes in video frames, which impair AR application detection and tracking, especially when accessibility features like subtitles or captioning occlude image anchors.
Innovation Solution
An automated system and method for detecting static image segments using machine learning models and techniques like optical flow or frame subtraction, allowing for the identification of anchor segments that meet size, contrast, and texture criteria, enabling AR effect placement without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection methods are used to identify image anchors for AR effects, then the efficiency and consistency of anchor selection is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The image processing is divided into distinct stages: initial anchor candidate identification, occlusion detection, and static region verification. Each stage processes only relevant portions of the data, reducing overall computational complexity while maintaining high detection efficiency through specialized algorithms for each subtask.
Solution Approach 2:
The system performs preliminary analysis to identify potential anchor regions and occlusion patterns before final anchor selection. By pre-processing images to detect static regions and potential occlusions early in the pipeline, the system reduces the computational burden on subsequent processing stages while ensuring efficient anchor detection.
2Adaptability or versatility
If accessibility features like subtitles or captioning are added to video content, then user accessibility is improved, but the availability of clear image anchors for AR tracking deteriorates due to occlusion
Solution Approach 1:
The system introduces an intermediary analysis layer that detects the presence of accessibility features like subtitles and captioning. This intermediary detection mechanism identifies occluded regions and excludes them from anchor candidate selection, allowing accessibility features to coexist with reliable AR tracking by mediating between the two requirements.
Solution Approach 2:
The system applies different quality criteria to different regions of the image based on local characteristics. Regions containing accessibility features are evaluated separately from clear regions, allowing anchors to be selected from suitable areas while preserving accessibility features in their designated areas without compromising either functionality.
3Measurement precision
If manual selection of image anchors is performed, then the precision of anchor selection is improved, but the time consumption and labor requirements increase
Solution Approach 1:
The system performs self-service by automatically analyzing video content to identify suitable anchor regions based on predefined criteria for static regions and clarity. The automated detection algorithms evaluate multiple candidate regions and select optimal anchors without human intervention, achieving both high precision through systematic evaluation and efficiency through automation.
Solution Approach 2:
The manual mechanical process of anchor selection is replaced with automated computational algorithms. Machine learning models and image processing algorithms substitute for human visual inspection and selection, maintaining high precision through systematic analysis while dramatically reducing time consumption and labor requirements.
4Adaptability or versatility
If video frames with dynamic changes are used for AR effects, then the engagement and interest of users are improved, but the ability to detect and track media content deteriorates
Solution Approach 1:
The system dynamically evaluates each video frame to identify regions with appropriate motion characteristics. By analyzing motion vectors and temporal changes, the system selects anchor regions that exhibit suitable stability for tracking while allowing the rest of the video content to maintain its dynamic and engaging nature. This dynamic selection process ensures reliable tracking even in highly dynamic video sequences.
Data Source
AI summary
A system includes a hardware processor and a system memory storing software code. The hardware processor is configured to execute the software code to receive reference content including a plurality of images in sequence, analyze the plurality of images to identify pixel feature changes between sequential images of the plurality of images, and identify, based on the pixel feature changes, one or more static image segments within the plurality of images. The hardware processor is further configured to execute the software code to evaluate the one or more static image segments using at least one of a size criterion or a visual feature criterion, select, based on evaluating, one of the one or more static image segments as an anchor segment, and provide mapping data identifying a location of the anchor segment within each of the plurality of images.