Video Object Jumplinks Using Multimodal Frame Identification
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Solution Overview
Problem
Existing video systems lack precision in identifying and linking to specific frames containing objects of interest, often requiring manual tagging that is prone to errors and failing to account for textual similarities, and do not seamlessly integrate object detection with video scrubbers.
Innovation Solution
Implementing a multimodal machine learning approach that combines image and textual analysis to accurately identify objects in video frames, using a consensus algorithm to enhance detection accuracy and create precise jumplinks, allowing users to directly navigate to relevant frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used to identify objects in video frames, then the system can link to specific frames, but the precision of object identification deteriorates due to human error and lack of textual analysis
Solution Approach 1:
The patent replaces manual tagging (mechanical human operation) with an automated multimodal machine learning system that combines image recognition and textual analysis. This substitution eliminates human error in tagging while maintaining the ability to link to specific video frames, thereby improving both precision and reliability of object identification.
Solution Approach 2:
The patent employs a composite approach by integrating multiple detection modalities (image analysis + textual analysis) into a unified object identification system. This composite methodology leverages the strengths of both image-based and text-based recognition to achieve higher accuracy than either modality alone, directly addressing the precision-reliability contradiction.
2Measurement precision
If traditional object detection is used without textual analysis, then the system operates faster, but the accuracy of identifying the correct object deteriorates
Solution Approach 1:
The patent creates a multi-functional detection system that simultaneously performs image analysis, textual analysis, and object identification through a unified machine learning model. This universal approach allows the system to leverage multiple data sources (visual and textual) without requiring separate processing pipelines, thereby improving accuracy while managing complexity through integration rather than multiplication of components.
Solution Approach 2:
The patent introduces a consensus algorithm as an intermediary that reconciles results from image-based detection and text-based detection. This mediator component synthesizes information from both modalities to produce a final object identification, enabling the system to achieve high accuracy through coordinated multi-modal analysis without overwhelming complexity.
3Loss of time
If viewers watch the entire video to find objects of interest, then they ensure complete coverage, but the time required to locate specific content increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-identifying all objects of interest in the video and creating jumplinks to their specific frames during video processing. This advance preparation allows viewers to immediately navigate to desired content without watching the entire video, dramatically reducing navigation time while maintaining productivity by enabling direct access to relevant segments.
Solution Approach 2:
The patent introduces jumplinks as an intermediary mechanism between the viewer and video content. Thesejumplinks serve as direct pathways to specific frames containing objects of interest, eliminating the need for linear video consumption and enabling efficient navigation while preserving complete content availability for those who wish to view the entire video.
Data Source
AI summary
Devices, systems, and methods for generating and presenting jumplinks for objects in video may include generating, by a language machine learning model, textual summaries of audio present in video frames, the textual summaries indicative of objects represented in the video frames; identifying unique identifiers of the objects indicated in the textual summaries; determining, by the language machine learning model, textual similarity scores between the information and the textual summaries; selecting, based on textual similarity scores, an object corresponding to a unique identifier and likely to be represented in one or more of the video frames; generating a jumplink for the video, the jumplink corresponding to a video frame in the video where the object is represented; and presenting the video and an indication that the jumplink corresponds to the video frame.