Multi-Protocol Video Ingest & Stream Management
Connect industrial and standard cameras across your facility. Hardware-accelerated decoding offloads CPU memory bottlenecks, keeping video pipelines stable during continuous operation.
Standard Camera Support
Native ingest for RTSP, GigE Vision, USB3 Vision, and MIPI CSI sensor feeds.
Hardware Decoding Offload
Direct GPU decoding reduces host CPU utilization and memory copying.
Resilient Reconnection
Automatic stream recovery and dynamic buffering during transient network drops.
Normalized Frame Buffers
Converts mixed camera feeds into consistent tensor formats for downstream inference.
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Edge-Optimized Neural Model Runtime
Run modern vision models on your own edge devices. Quantization and batching tools help maintain stable frame rates on NVIDIA Jetson units and workstation GPUs.
INT8 & FP16 Precision
Quantize models to reduce memory and compute requirements with minimal accuracy loss.
Standard Framework Export
Direct support for models exported from PyTorch, ONNX, and TensorRT pipelines.
Dynamic Batch Processing
Balances throughput and responsiveness under variable camera workloads.
Multiple Vision Tasks
Configurable pipelines for object detection, segmentation, and pose tracking.
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Multi-Camera Spatial Coverage & Alert Zones
Calibrate multiple overlapping cameras to track objects across wider areas. Set up virtual safety zones and tripwires to notify your operations team when events occur.
Multi-Camera Calibration
Guided extrinsic calibration routines to align overlapping viewpoints.
Cross-Camera Tracking
Maintains object continuity as items or personnel move between camera fields of view.
Configurable Alert Boundaries
Define custom polygons for pedestrian lanes, restricted areas, and equipment zones.
Flexible Event Dispatch
Trigger alerts via webhooks, MQTT topics, visual alarms, or hardware relays.
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Local Network Deployment & Data Privacy
Process all video feeds entirely on your local facility network. Your video data never leaves your premises, avoiding cloud bandwidth fees and privacy risks.
100% On-Premise Execution
No external cloud dependencies required for continuous day-to-day operations.
Zero Cloud Video Egress Costs
Eliminates expensive monthly cloud bandwidth charges for multi-camera feeds.
Cluster Health & Failover
Monitor node status and reassign stream workloads if a host needs maintenance.
Standard Integration APIs
Export detection events and metadata directly to SCADA, MES, or robotics controllers.
Local Edge Processing vs. Cloud Streaming
Why on-premise vision pipelines provide better reliability, privacy, and cost predictability for operational environments.
| Aspect | Typical Cloud Vision | Perceptras On-Premise |
|---|---|---|
| Video Processing Location | Uploaded continuously to third-party cloud servers | Processed locally on your on-premise edge hardware |
| Bandwidth & Data Costs | High recurring costs for uploading multiple 4K/FHD streams | Zero internet bandwidth needed for video processing |
| Data Privacy & Compliance | Video leaves facility premises and enters external datacenters | Footage remains 100% on your secure local network |
| Internet Outage Impact | Vision system stops functioning if connection is interrupted | Continues running normally without external internet |
| Multi-Camera Coordination | Usually processes isolated frames with separate API calls | Fuses overlapping camera viewpoints in unified coordinate space |
| Integration with Local Systems | Requires webhooks back from the cloud with internet delays | Direct local buses: gRPC, Kafka, MQTT, and digital I/O signals |
