Video Object Attribute Management via Uniqueness Extraction
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Solution Overview
Problem
Existing systems face inefficiencies in managing and storing attributes associated with objects in video data, particularly when dealing with large datasets and irrelevant attributes, which complicates the identification of objects of interest.
Innovation Solution
A video processing service identifies and stores attributes for objects of interest based on uniqueness criteria, prioritizing attributes that uniquely identify objects and efficiently managing storage space by replacing or updating attributes as new data is obtained, allowing for effective matching and notification of similar objects across video data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all attributes are stored for every object in video data, then complete information is preserved, but storage efficiency deteriorates and object identification becomes more difficult
Solution Approach 1:
The patent extracts and stores only the most relevant and distinctive attributes for each object class, separating essential identification features from irrelevant or redundant attributes. This selective extraction maintains reliable object identification while significantly reducing storage requirements compared to storing all possible attributes.
Solution Approach 2:
The patent applies different attribute sets to different object classes based on their specific identification needs. Each object type (person, vehicle, animal) has a customized attribute profile that stores only locally relevant features, optimizing both storage efficiency and identification accuracy for each category.
2Device complexity
If a fixed set of attributes is stored for all objects, then database structure is simplified, but irrelevant attributes increase storage overhead and reduce efficiency
Solution Approach 1:
The patent segments the attribute database into class-specific attribute sets rather than using a single fixed structure for all objects. This segmentation allows each object class to store only its relevant attributes, reducing overall storage overhead while maintaining a manageable database structure through organized categorization.
Solution Approach 2:
The patent creates a universal attribute management system that adapts to different object classes by selecting appropriate attributes from a comprehensive pool. This multi-functional approach allows the same database framework to efficiently handle diverse object types without storing irrelevant attributes for each class.
3Loss of information
If all attributes are retained in the database, then no information is lost, but processing time increases and object matching becomes less efficient
Solution Approach 1:
The patent extracts and retains only the most discriminative attributes needed for effective object matching, removing redundant information that would slow down processing. This selective retention maintains sufficient information completeness for accurate identification while dramatically improving matching speed by reducing the attribute set that must be processed.
Solution Approach 2:
The patent applies partial action by storing more attributes than the absolute minimum needed, maintaining a buffer of relevant features that ensures robust object matching under various conditions while avoiding the excessive storage of all possible attributes. This balanced approach optimizes both information retention and processing efficiency.
Data Source
AI summary
Systems, methods, and software described herein manage descriptive attributes associated with objects in video data. In one example, a video processing service obtains video data and identifies an object of interest in the video data. The video processing service further identifies attributes for the object of interest from the video data based on the attributes satisfying uniqueness requirements to differentiate the object of interest from other objects. Once the attributes are identified, the attributes may be stored in a storage system to be compared against other objects in second video data.