event-rule-engine

You can automate video surveillance event detection by extracting frames from video files and applying LLM visual review. The system maps observed patterns to predefined event templates like fights or traffic accidents, generating event candidates with evidence frames and time ranges.

Frame extraction and LLM analysis can detect urban surveillance events including fights, traffic accidents, crowding, illegal parking, smoke and fire, falls, intrusion, and occupancy. The LLM reviews extracted frames to map visual patterns to these specific event templates.

Yes, processing video files for incident detection requires opencv-python and ffmpeg-python to extract frames. These dependencies handle video frame extraction, which is then passed to an LLM for visual analysis and event mapping.

Yes, LLM visual review is designed for urban surveillance systems to monitor incidents like traffic accidents in real time. By continuously extracting frames and analyzing them with an LLM, the system generates event candidates with evidence for quick response.

The best way to generate evidence for detected surveillance events is using an automated rule engine that extracts video frames, applies LLM visual review to identify patterns, and maps them to event templates with corresponding evidence frames and time ranges.

Limitations of using LLM visual review for video event detection include dependency on frame extraction quality and the LLM's ability to accurately interpret complex visual patterns. It requires both opencv-python and openai dependencies to function properly.