Crowd-grade recall
Detects individuals even when partially occluded in dense queues at turnstiles, gates, and terminal walkways.
Innomium Vision · Edge YOLO
Sentinel is Innomium's cutting-edge YOLO model — very light, very strong, and tuned for the hardest real-world scenes. From airport concourses to stadium gates, it finds every person with 92% accuracy.
Proven performance
Sentinel maintains high recall in dense, overlapping crowds — the kind of scenes where generic detectors miss people or flood you with false positives.
Detects individuals even when partially occluded in dense queues at turnstiles, gates, and terminal walkways.
Scoring-aware post-processing and TTA consensus keep high-confidence detections clean in complex scenes.
A compact 19 MB ONNX model suitable for edge cameras, kiosks, and in-browser demos without a GPU farm.
Capabilities
Dynamic thresholds respond to scene density — maximizing recall when few people are present and precision when the crowd thickens.
~19 MB footprint loads fast and runs on CPU via ONNX Runtime — in the cloud, on edge hardware, or directly in the browser.
Test-time augmentation cross-validates detections across flipped views to reduce false alarms before they reach your pipeline.
This demo runs inference entirely in your browser. Frames never leave your device — ideal for regulated environments.
Use cases
Passenger flow, queue monitoring, and gate-level occupancy in high-throughput terminals.
Entry-point crowd analysis, congestion detection, and real-time headcount at scale.
Foot traffic analytics and zone density with a model small enough for in-store edge boxes.
Situational awareness for campuses, plazas, and smart-city pilot deployments.
Interactive demo
Run the production ONNX model in your browser — same pipeline, TTA and adaptive confidence included.
[]
Very light. Very strong. 92% accuracy in crowds. Built by Innomium for airports, venues, and edge deployments that cannot afford to miss a person.