Solution
AMIS builds real-time perception pipelines that convert video into structured events, risk signals, and decisions teams can act on.
Core capabilities
A real computer vision intelligence pipeline output produced by AMIS, showing detection, tracking, and event-level reasoning in real time.
The system detects and tracks people across frames, extracts events, and surfaces decision-ready signals with confidence and timing context.
This demo showcases a real-time computer vision pipeline detecting and tracking multiple objects (people and vehicles) in a busy public space. Each detected object receives per-frame confidence scores, continuous tracking IDs, and event-level classifications. The system converts raw video frames into structured, decision-ready signals that downstream systems can consume immediately.
Each detected object generates a structured event. Here's a sample of what the system produces:
{
"timestamp": "2025-01-01T14:23:45.678Z",
"frame_id": 847,
"object_id": "obj_12847",
"class": "person",
"confidence": 0.94,
"bbox": {
"x": 245,
"y": 180,
"width": 85,
"height": 160
},
"tracking_status": "tracked",
"event_type": "object_detected",
"event_subtype": "continuous_presence"
}High-quality video annotation for machine learning and deep learning, enabling faithful AI development across industries.
AMIS provides scalable, accurate video annotation services for computer vision training data at cost-effective pricing.
This showcase demonstrates AMIS's professional video annotation service, where raw video frames are meticulously labeled and annotated to create high-quality training datasets for machine learning and deep learning models. Our annotation workflow includes object detection, tracking, pose estimation, and semantic labeling tailored to each industry's specific requirements.
Annotated video data is exported in structured formats ready for model training. Here's a sample annotation record:
{
"frame_id": 247,
"timestamp": "2025-01-01T12:15:30.456Z",
"annotations": [
{
"object_id": "obj_5847",
"class": "person",
"bbox": {
"x": 150,
"y": 200,
"width": 70,
"height": 140
},
"confidence": 1
},
{
"object_id": "obj_5848",
"class": "vehicle",
"bbox": {
"x": 450,
"y": 180,
"width": 200,
"height": 120
},
"confidence": 1
}
],
"annotation_status": "validated",
"annotator_id": "ann_2847",
"validation_date": "2025-01-01T14:00:00.000Z"
}Raw video is noisy, unstructured, and difficult to operationalize. Dashboards flood teams with alerts that lack context, and most CV systems cannot explain why an alert happened. Without trust, teams ignore signals or disable automation entirely.
Video -> Perception models -> Temporal reasoning -> Event extraction -> Decisions and actions.
Ingest RTSP, VMS, or edge feeds with synchronization and metadata normalization.
Run detection, tracking, and segmentation models with confidence and filtering.
Link frames into behaviors, sequences, and scene context with explainable logic.
Convert signals into structured events with severity, timing, and evidence.
Trigger alerts, workflows, and human review with clear decision traces.
Operational outputs you can integrate and trust.
Turn video into normalized event records and scene summaries.
Actionable notifications with evidence and operator-ready context.
Integrate events into downstream systems and approvals.
Beyond detection, we deliver event intelligence for operations.
Where visual intelligence drives real-world decisions.
Deploy on the edge or in the cloud, integrate your preferred models, and connect outputs to the systems your teams already use.
See how AMIS converts perception into trusted operational actions.