Crowd-Sourced Video Object Tagging System
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Solution Overview
Problem
Current methods for object identification and tracking in videos, especially in low-resolution videos with varying environmental conditions, are computationally challenging and inefficient, particularly when objects move at high speeds or have low frame rates, limiting effective metadata generation and retrieval.
Innovation Solution
A system utilizing crowd sourcing for real-time object tracking in videos broadcasted on TV, where users can indicate bounding boxes to tag objects, with additional metadata generated and augmented by multiple users across a social community network, leveraging cloud computing for image analysis and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging methods are used for object identification in videos, then tagging accuracy can be improved, but the complexity of the system and time consumption increase significantly
Solution Approach 1:
The system enables users to automatically tag objects in videos by having them draw bounding boxes around objects of interest. The crowd-sourced annotations from multiple users are then automatically processed to generate metadata, eliminating the need for complex manual tagging systems while maintaining high accuracy through collective user input.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives bounding box annotations from multiple users and automatically generates structured metadata. This intermediary system bridges the gap between simple user actions (drawing boxes) and complex metadata generation, reducing both system complexity and time consumption while maintaining tagging accuracy.
2Productivity
If crowd sourcing technique is used for object tagging, then tagging efficiency and accuracy improve, but the system complexity and coordination overhead increase
Solution Approach 1:
The system merges multiple user annotations of the same object into a single consolidated metadata entry. By combining bounding box data from multiple users and automatically generating unified metadata, the system achieves high tagging efficiency while managing complexity through automated consolidation processes rather than manual coordination.
Solution Approach 2:
The patent uses copying by having multiple users independently annotate the same video content, creating multiple copies of annotation data that are then processed. This approach improves efficiency through parallel processing of identical tasks while the automated system handles the complexity of merging and validating the copied annotations.
3Speed
If automated image processing is used for metadata generation, then processing speed improves, but accuracy decreases due to computational challenges in low-resolution videos
Solution Approach 1:
The system performs preliminary actions by having users draw bounding boxes around objects of interest before automated processing begins. This pre-segmentation provides accurate spatial information that guides the subsequent automated image processing, enabling fast processing while maintaining high accuracy even in low-resolution videos by focusing computational resources on predefined regions.
Data Source
AI summary
A method and system for tracking of objects in a video is disclosed. The method of the present invention enables user to indicate a boundary-box to the identified object of interest in the broadcast video on television or any other communication media. The object indicted in the boundary-box is than tracked by the users connected in a social community network in the upcoming video frames of the broadcasted video. The tracked object is then tagged by the users in the social community network. Further, the present invention enables augmentation of the tracked object in the video by extracting additional information from the online service providers. The augmentation and tagging of the object generates metadata related to the object. The metadata generated is stored on a server to track the object in future based on the metadata related to the object.