Video Hazard Prediction via Deep Learning Annotation and Repository Comparison

The system segments the complex video analysis task into distinct functional modules: video data reception module, annotation module using deep learning tools, analysis module for identifying dangerous conditions, comparison module for matching with repository videos, scoring module for similarity assessment, and action generation module. This segmentation allows each module to handle specific aspects of the analysis independently, managing overall system complexity while achieving reliable safety prediction.

The patent introduces an intermediary database/repository that stores pre-annotated videos of known dangerous conditions. This intermediary structure serves as a reference library that mediates between the incoming video analysis and the final safety determination, allowing the system to compare analyzed videos against known patterns without requiring complex real-time classification of all possible hazards.

The system performs preliminary annotation of videos in advance and stores them in a repository with pre-identified dangerous conditions. When analyzing new video data, the system compares against these pre-annotated references rather than performing full deep learning annotation in real-time. This preliminary action significantly reduces processing time while maintaining detection precision through the comparison with known patterns.

Instead of performing complete deep learning annotation on every video frame in real-time, the system applies partial annotation by comparing key features against the pre-annotated repository. This partial action approach focuses computational resources only on critical comparison operations rather than full annotation, reducing processing time while maintaining sufficient detection accuracy.

The system implements self-service automation where the computer automatically generates and executes mitigation actions when the similarity score exceeds the threshold, without requiring human intervention. The system serves itself by autonomously completing the full workflow from video analysis through to action generation, improving hazard mitigation productivity while managing complexity through automated decision rules.

The system incorporates feedback mechanisms where the results of automated actions are monitored and used to refine future analysis. The similarity scoring system provides feedback on detection confidence, and the threshold mechanism provides feedback-based decision making. This feedback loop improves productivity by enabling continuous automated operation while managing complexity through established feedback control patterns.