Video Stream Behavioral Analysis via Time Anchors
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Solution Overview
Problem
Current video management systems are monolithic, inefficient in scaling, and lack the ability to effectively detect events of interest and produce accurate video summarizations, particularly in scenarios with many locations and few cameras, and they fail to provide meaningful behavioral analysis by aggregating data over long periods, which leads to temporal flattening of behavioral cues.
Innovation Solution
A system and method that decompose video streams into salient fragments, build a database of these fragments, and use time anchors to retrieve and generate focalized visualizations, tag human subjects, and analyze behavior to provide meaningful behavioral scores, allowing for focused analysis on specific events or keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video streams are analyzed using traditional monolithic architecture, then system simplicity is maintained, but scalability and efficiency deteriorate when deploying many locations with few cameras
Solution Approach 1:
The patent segments video stream analysis into distinct functional modules: event detection module, time anchor generation module, video summarization module, and behavioral analysis module. Each module processes specific aspects independently, allowing the system to scale across multiple locations without requiring complete system redesign, thus improving adaptability while managing complexity through functional separation
Solution Approach 2:
The patent introduces a temporal dimension through time anchors that mark specific events in video streams. This allows the system to organize and retrieve video data not just by spatial location but by temporal significance, enabling efficient scaling across many locations by providing an additional organizational dimension beyond physical deployment
2Quantity of substance
If behavioral data is aggregated over long periods, then comprehensive analysis is achieved, but temporal flattening of behavioral cues occurs
Solution Approach 1:
The patent performs preliminary action by detecting events and generating time anchors during video processing, before behavioral aggregation occurs. These time anchors pre-mark significant moments, allowing subsequent behavioral analysis to focus on relevant temporal windows rather than aggregating all data uniformly, thus preserving temporal precision while maintaining comprehensive coverage
Solution Approach 2:
The patent applies local quality by analyzing behavioral cues with different temporal resolutions at different time points. Around detected events and time anchors, the system uses finer temporal granularity to capture precise behavioral changes, while between events it uses coarser aggregation. This localized adaptation of analysis depth maintains measurement precision for critical moments while enabling comprehensive long-term analysis
3Productivity
If video streams are distributed using connection-centric routing, then system simplicity is maintained, but event detection efficiency and summarization accuracy deteriorate
Solution Approach 1:
The patent extracts event detection functionality from the traditional video distribution pipeline and places it at strategic points in the system architecture. Event detectors are positioned to identify significant moments without requiring complete video routing through complex paths, improving detection efficiency while the extracted event data is used to generate time anchors that guide subsequent summarization processes
Solution Approach 2:
The patent introduces time anchors as intermediary elements that mediate between video stream distribution and behavioral analysis. These time anchors serve as markers that guide where attention should be focused in the video streams, allowing the system to efficiently locate and analyze relevant events without requiring complex routing of entire video streams, thus improving productivity while managing architectural complexity
Data Source
AI summary
A system and method for analyzing behavior in a video is described. The method includes extracting a plurality of salient fragments of a video; building a database of the plurality of salient fragments; receiving a keyword; identifying a time anchor when the keyword appears in an audio track associated with the video; retrieving one or more salient fragments of the video from the database of the plurality of salient fragments based on the time anchor; generating a focalized visualization based on the one or more salient fragments of the video; tagging a human subject in the focalized visualization with a unique identifier; analyzing the focalized visualization based on the time anchor and the unique identifier to generate a behavior score; and providing the behavior score via the user device.