Activity Classification via Location Graphs from Video Metadata
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Solution Overview
Problem
Existing video analysis techniques are computationally expensive and inefficient in determining activities performed during video capture, particularly when dealing with large datasets and diverse video content.
Innovation Solution
A system comprising physical processors configured with machine-readable instructions, including a video component, location graph component, and activity component, to analyze metadata and tracking information from video files, generating location graphs and identifying graph attributes to determine activity types performed by entities with a capture device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video analysis techniques are used to determine activities performed during video capture, then activity classification can be achieved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent extracts and processes only the most critical metadata fields (location, timestamp, duration) rather than analyzing the entire video content. By taking out only the essential tracking information from the video data, the system achieves activity classification without the computational burden of full video analysis, thus resolving the contradiction between accuracy and computational cost
Solution Approach 2:
The patent segments the video analysis task into discrete metadata fields (location data, temporal information, duration) that can be processed independently and efficiently. This segmentation allows the system to analyze activity patterns through combinatorial logic of discrete fields rather than processing continuous video streams, significantly reducing computational requirements while maintaining classification accuracy
2Measurement precision
If comprehensive video content analysis is performed to improve activity detection accuracy, then better classification results are achieved, but processing time increases
Solution Approach 1:
The patent performs preliminary extraction and preprocessing of metadata fields before activity classification. By preparing and organizing the essential tracking information (location sequences, timestamps, durations) in advance, the system enables rapid activity determination without reprocessing the entire video content, thus reducing processing time while maintaining detection accuracy
Solution Approach 2:
The patent creates a simplified representation (copy) of video activity information using metadata fields that capture the essential characteristics of video content. This metadata copy contains location, time, and duration information that suffices for activity classification without requiring analysis of the actual video pixels or audio streams, thereby achieving fast processing with maintained accuracy
3Adaptability or versatility
If detailed video content processing is applied to handle diverse video content, then activity classification improves, but system complexity increases
Solution Approach 1:
The patent creates a universal activity classification framework that handles diverse video content types (sports, entertainment, news, etc.) through a common metadata processing approach. By using a unified set of metadata fields (location, timestamp, duration) and consistent classification logic, the system can adapt to various video domains without requiring separate complex processing pipelines for each type, thus reducing overall system complexity while maintaining versatility
Data Source
AI summary
Systems and method of determining one or more activities performed during video capture are presented herein. Information defining a video may be obtained. The information defining the video may include content information, metadata information, and/or other information. The content information may define visual content of the video and/or other content of the video. The metadata information may include tracking information and/or other information. The tracking information may locations of the capture device as a function of progress through the video. One or more activity types being performed by an entity moving with the capture device during the previous capture may be determined based on the tracking information. For example, a location graph may be generated from the tracking information. The location graph may be used to determine one or more activity types that were performed.