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 full file transmission to servers for processing, which is costly and time-consuming, and lack interactivity, especially when users attempt to capture or browse media content.
Innovation Solution
A system utilizing multiple processors to perform parallel object detection and recognition on media content items, with features like recency-based sorting, prioritization of media content, and local object model storage for efficient processing and recognition, including face detection and live camera view processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches transmit full media content files to servers for processing, then object detection can be performed, but processing time and server load increase significantly
Solution Approach 1:
The patent extracts only the necessary portions of media content (specific regions containing objects) rather than transmitting entire files. This is achieved through selective transmission of object-containing regions to servers for processing, significantly reducing data volume while maintaining detection accuracy.
Solution Approach 2:
The patent segments media content processing into multiple stages: initial processing at client devices using multiple processors, pre-filtering to identify objects of interest, and selective transmission to servers. This segmentation allows parallel processing at client level while reducing server workload.
2Productivity
If conventional approaches process all media content items sequentially, then processing can be completed, but overall processing speed decreases
Solution Approach 1:
The patent divides the media content set into multiple subsets that can be processed in parallel by different processors. Each processor handles a specific subset simultaneously, transforming sequential processing into a parallel architecture that dramatically improves throughput while reducing total processing time.
3Speed
If object detection is performed with high CPU priority, then processing speed increases, but other system operations may be affected
Solution Approach 1:
The patent applies partial action by performing object detection at lower CPU priority levels rather than maximum priority. This approach provides sufficient processing speed for most applications while maintaining system stability and allowing other critical operations to function properly, avoiding the need for excessive CPU resource allocation.
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.