The problem
Teams cannot watch every camera continuously. Important events are easily buried in hours of routine footage, while conventional motion alerts produce too much noise to guide a fast response.
The approach
YOLOv8 detects objects, pose estimation adds human movement context, and NVIDIA DeepStream keeps the multi-stream pipeline efficient. Python services apply event rules and pass prioritized alerts to downstream systems.
Implementation
The pipeline ingests live feeds, keeps track of relevant subjects across frames, evaluates movement against operational rules, and emits structured events for review or integration with an existing response workflow.
What we learned
- Useful video analytics requires explicit operational thresholds and escalation paths; detection alone does not create a response system.
- Edge and stream constraints should shape model choice early, especially when several feeds must run with predictable latency.