Parallel Object Detection in Media Content Processing
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Solution Overview
Problem
Conventional approaches for processing media content, such as images, are inefficient, requiring significant time and server resources due to the need for full file transmission and remote processing, which can lead to inconvenient and uninteractive user experiences, especially when dealing with object recognition and browsing through multiple media items.
Innovation Solution
The system utilizes multiple processors in parallel for object detection and recognition within media content items, prioritizes recent items, and selectively performs object detection based on user interactions and object popularity metrics, allowing for local processing and efficient identification of objects within media content, including face detection and live camera views with real-time labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional approaches are used for media content processing, then object detection can be performed, but processing time and server resources increase significantly
Solution Approach 1:
The patent segments the object detection task by dividing media content items into different processing groups based on recency and user interaction status. Recent items and interacted items are processed separately from older items, allowing the system to prioritize time-sensitive content and reduce overall processing time for critical items.
Solution Approach 2:
The system performs preliminary actions by pre-processing media content items to determine recency data and user interaction history before actual object detection. This preliminary sorting and categorization enables the system to optimize processing order and allocate resources more efficiently, reducing total processing time.
2Ease of operation
If full file transmission and remote processing are performed, then complete object recognition is achieved, but user interaction convenience deteriorates
Solution Approach 1:
The patent extracts and processes only the necessary portions of media content items based on recency and user interaction data. By extracting only recent items and items with user interactions for prioritized processing, the system reduces transmission and processing requirements while maintaining user interaction convenience for the most relevant content.
Solution Approach 2:
The system dynamically adjusts processing priorities based on real-time user interaction data. When a user interacts with media content, the system dynamically re-prioritizes the processing queue to ensure that interacted items are processed first, making the system adaptive to user needs and improving interaction convenience.
3Reliability
If object detection is performed on all media content items, then comprehensive object recognition is achieved, but system resource consumption increases
Solution Approach 1:
The patent applies local quality by differentiating processing requirements for different media content items based on their recency and user interaction status. Recent items and interacted items receive prioritized processing with higher resource allocation, while older items are processed with reduced resources or deferred, optimizing resource consumption while maintaining recognition completeness for critical content.
Data Source
AI summary
Access to a set of media content items is acquirable. Identified processors can perform, in parallel, object detection for the set. In some cases, information about a current system state, a user, and/or object popularity metrics is acquirable for selecting a subset of object models. Object recognition is performable, based on the subset, for the set of media content items. In some instances, a camera view can be provided. Object recognition is performable for representations of the view. An object depicted in the representations is identifiable. An interface portion is presentable to provide a label for the object. In some cases, object recognition is performable for the set of media content items to identify an object depicted in a content item. A label is associable with the content item. A search through the set of media content items can identify, based on the label, a subset that depicts the object.