Parallel Object Mining for Video Files
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for object mining and recognition from digital collections face challenges in efficiently processing dynamic structures and maintaining sequential ordering of identified objects, particularly in video files, due to resource-intensive processing requirements and difficulties in handling changes in file formats and metadata.
Innovation Solution
A system and method that employs parallel and asynchronous processing of video files by segmenting them into uniform buffers, using an encoder to format and preprocess the segments, and a manager to analyze and reorder the identified frames, ensuring efficient resource utilization and maintaining the original sequence of objects within the output file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video files are processed sequentially for object mining and recognition, then processing accuracy is maintained, but processing time and resource utilization are excessive
Solution Approach 1:
The video file is divided into multiple segments that can be processed independently and in parallel. Each segment is processed by separate analysis services, enabling concurrent object mining and recognition operations across different portions of the video file, thereby significantly reducing total processing time while maintaining accuracy through consistent formatting of each segment.
Solution Approach 2:
The video file undergoes preliminary formatting and segmentation before the actual object mining and recognition process. This preprocessing step organizes the video into uniformly formatted segments with proper metadata, enabling efficient parallel processing in subsequent stages without compromising the accuracy of object identification.
2Productivity
If multiple analysis services process video segments in parallel, then processing efficiency is improved, but maintaining sequential ordering of identified objects becomes difficult
Solution Approach 1:
The system implements feedback mechanisms where processed segments are continuously monitored and their output sequences are tracked. This feedback enables the system to maintain proper sequential ordering of identified objects across parallel processing streams by adjusting and coordinating the output from different analysis services based on their processing status and segment position.
Solution Approach 2:
A coordinator component acts as an intermediary between parallel analysis services and the final output. This intermediary receives results from multiple parallel services, reconciles their outputs, and ensures that identified objects are presented in the correct sequential order, thereby maintaining precision while enabling parallel processing efficiency.
3Productivity
If video files are segmented into multiple buffers for parallel processing, then resource utilization is maximized, but handling dynamic structures and format changes becomes complex
Solution Approach 1:
The system employs parameter changes by dynamically adjusting segment boundaries, buffer sizes, and processing configurations based on the characteristics of the video file and available resources. This allows the system to handle dynamic structures and format changes effectively while maintaining optimized resource utilization across parallel processing operations.
Data Source
AI summary
Embodiments are directed to a system, computer program product, and method for dynamic object mining. A received file is segmented, re-formatted, and organized into buffers, while maintaining the order of the received file. Multiple buffers are processed in parallel for object mining. A listener is encoded into the buffers to support asynchronous processing, and more specifically ordering of mined objects. An output file of the mined objected or associated frames is created. The file is populated with a sequential ordering of the objects that follows the order of the received file.