Hierarchical Object Identification for Low-Latency Video Augmentation
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Solution Overview
Problem
Conventional augmented reality systems face challenges in efficiently identifying and augmenting video content in real-time, particularly in detecting multiple objects within a video frame and providing low-latency augmentations, which limits their capacity and robustness.
Innovation Solution
An object identification system that employs hierarchical, multi-stage subsystems for object classification and feature-based recognition, utilizing machine learning models and edge servers for low-latency network-based object identification and video content augmentation, enabling concurrent processing of multiple objects and efficient augmentation rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional augmented reality systems process video content in real-time, then low-latency augmentation is achieved, but the system capacity and robustness are limited
Solution Approach 1:
The patent divides the object identification process into multiple hierarchical stages: first detecting objects at a coarse level, then refining identification at finer levels. This segmentation allows the system to handle complex scenes with multiple objects efficiently, improving both robustness and capacity by processing objects in manageable increments rather than attempting to analyze everything simultaneously.
Solution Approach 2:
The patent introduces a hierarchical dimension to object identification, moving from coarse-level detection to fine-level recognition. This dimensional approach enables the system to process multiple objects of varying complexity concurrently, thereby increasing system capacity while maintaining real-time performance and robustness.
2Productivity
If the system detects multiple objects within a video frame, then object detection capacity increases, but processing time and latency increase
Solution Approach 1:
The patent segments the video processing into discrete frames and processes objects within each frame independently through hierarchical stages. This allows parallel processing of multiple objects across different frames, maintaining high detection capacity while minimizing processing latency through efficient frame-by-frame analysis.
Solution Approach 2:
The patent performs preliminary object detection at coarse levels before proceeding to fine-level identification. This preliminary action filters and prioritizes objects for detailed analysis, reducing the processing time required for each object while maintaining the ability to detect multiple objects simultaneously.
3Measurement precision
If hierarchical multi-stage subsystems are used for object classification, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the identification system into distinct hierarchical stages, each handling specific aspects of object recognition. This segmentation improves accuracy by focusing computational resources at appropriate levels while managing complexity through modular, organized processing stages that build upon each other systematically.
Data Source
AI summary
An exemplary object identification system detects, based on a machine learning model, an object depicted within a video frame. The system identifies, based on the detecting of the object, a class label of the object and a region of interest, within the video frame, of the object. The system identifies, within the region of interest of the object, set of features of the object. The system compares the set of features of the object with a plurality of predefined features within a data store associated with the class label of the object. The system determines, based on the comparing of the set of features of the object with the plurality of predefined features within the data store, whether the object is configured to trigger an augmentation of video content associated with the video frame. Corresponding methods and systems are also disclosed.