HydraVision: Next-Gen Multi-Camera Surveillance Platform

HydraVision Integration Guide: Stream, Store, and Analyze Video

Date: February 7, 2026

This guide shows a practical, end-to-end approach to integrating HydraVision into your video pipeline: streaming cameras, storing footage efficiently, and running analytics for object detection, tracking, and insights. It assumes HydraVision is a multi-camera video-management and analytics platform (on-prem or cloud) exposing standard ingestion APIs and supports common storage and AI integrations. Where a choice is needed, reasonable defaults are provided.

1. Architecture overview

• Edge cameras capture video (RTSP/HLS/ONVIF).
• Ingest layer (HydraVision Gateway or agent) receives streams, normalizes formats, and forwards to processing and storage.
• Storage: hot store for recent footage (object-based or block storage), cold archive for long retention (object storage with lifecycle rules).
• Processing: real-time analytics at the edge or centralized GPU nodes for detection, tracking, and metadata extraction.
• Index & search: metadata database and time-series index for fast queries.
• Client apps: live viewing, playback, alerts, dashboards, and API access.

2. Pre-integration checklist

• Inventory cameras: model, resolution, codec, frame rate, stream URL (RTSP/HLS), ONVIF support.
• Network capacity: estimate upstream bandwidth per camera: bitrate ≈ resolution × fps × compression factor. Plan for peak.
• Security: TLS for control plane, SRTP/DTLS for media where supported, firewall rules, and service account credentials.
• Storage requirements: retention days, retention policy, expected storage per camera per day.
• Compute sizing: number of concurrent stream decoders and analytics models (GPU/CPU).
• Compliance: data residency, retention, and redaction rules.

3. Ingest: connecting streams

1. For ONVIF-capable cameras: enable ONVIF, retrieve RTSP URL via HydraVision Gateway discovery.
2. For RTSP-only cameras: register stream URL in HydraVision Console with camera metadata (location, name, tags).
3. For cloud/hybrid: use HydraVision Agent to forward encrypted streams to cloud ingest, or configure secure peering/VPN between sites.
4. Configure adaptive bitrate (ABR) or substreams: send a low-res substream for monitoring and high-res for analytics/archival.
5. Validate ingest: check frame drops, latency, and codec compatibility.

4. Storage: hot and cold tiers

• Hot storage (recent 7–30 days)
  • Use fast object or block storage (NVMe-backed or SSD-backed volumes).
  • Store keyframe-aligned chunks (e.g., 1–5 minute segments) and MP4/TS containers for easy playback.
  • Keep corresponding per-chunk metadata and checksum.
• Cold storage (archive)
  • Use cloud object storage (S3-compatible) with lifecycle rules to transition to infrequent/Glacier-like tiers.
  • Store video in compressed, chunked files with sidecar metadata (JSON) containing timestamps, camera id, and extracted events.
• Retention & deletion
  • Configure automated lifecycle policies per camera/tag and ensure secure deletion where required.

5. Real-time analytics pipeline

• Edge vs centralized processing
  • Edge: run lightweight models (person detection, license-plate capture) on-site to reduce bandwidth and latency.
  • Central: run heavier models (multi-camera tracking, re-identification, behavior analysis) on GPU clusters.
• Design pattern
  1. Ingest frames -> pre-process (resize, normalize)
  2. Run detection models (YOLOv8 or equivalent)
  3. Run tracking (DeepSORT/ByteTrack) and attribute classifiers
  4. Store events and thumbnails in index; persist annotated video or overlays optionally
  5. Trigger alerts/webhooks when rules match
• Performance tips
  • Use batched inference for throughput on GPU.
  • Quantize models (INT8) where latency-critical.
  • Cache model pipelines and reuse preprocessing across models.

6. Metadata, indexing, and search

• Event schema: timestamp, camera_id, event_type, bounding_box, confidence, attributes, thumbnail_url, storage_chunk_ref.
• Indexing: time-series index for events (e.g., Elasticsearch, OpenSearch, or specialized vector DB for embeddings).
• Search use-cases: time-range queries, person/vehicle re-identification, attribute filtering (color, clothing).
• Retention & GDPR: support redaction or automated purge of events tied to PII.

7. Integration points and APIs

• Ingest API: register cameras, health-checks, stream metadata, and start/stop ingest.
• Storage API: put/get chunked video, list objects, lifecycle management.
• Analytics API: submit frames for inference, stream inference results, subscribe to alerts via webhooks.
• Search API: query events, fetch thumbnails, and retrieve related video segments.
• WebSocket: low-latency notifications for live alerts and state changes.
• Authentication: OAuth2 service accounts and short-lived tokens for agents.

8. Monitoring, logging, and alerting

• Monitor: ingest latency, frame drop rate, CPU/GPU utilization, storage consumption, and queue latencies.
• Log: standardized structured logs for stream sessions and model inference with unique trace IDs.
• Alerts: set thresholds for dropped frames, pipeline backpressure, or model confidence degradation.

9. Deployment example (reasonable defaults)

• Small site (10 cameras): HydraVision Gateway on 4 vCPU, 8 GB RAM; 1 local NVR with 4 TB SSD; edge node with a Jetson-class device for lightweight analytics.
• Medium (100 cameras): Gateway cluster (3x nodes), central GPU server (1x NVIDIA A10 or A30), 50 TB hot object storage, S3-compatible cold archive.
• Large (1000+ cameras): Kubernetes-based HydraVision cluster, multi-GPU inference farm, distributed object storage with erasure coding, multi-region failover.

10. Security and compliance checklist

• Encrypt data in transit and at rest.
• Use network segmentation and least privilege for service accounts.
• Maintain audit logs and access controls for playback and exports.
• Redaction/tokenization for PII where required; document retention policies.

11. Troubleshooting common issues

• High frame drops: check network bandwidth, camera bitrate, and gateway CPU.
• Missing metadata: verify camera registration and time sync (NTP).
• Slow searches: optimize indexing shards, use time-based indices, and pre-aggregate common queries.
• Model drift/low accuracy: retrain with site-specific data and validate with a labeled sample set.

12. Next steps and checklist for rollout

1. Complete camera inventory and network assessment.
2. Deploy HydraVision Gateway/Agent in a test environment.
3. Connect 5–10 pilot cameras, enable ABR and analytics.
4. Measure bandwidth, storage, and model performance for 2 weeks.
5. Iterate sizing and scale to production with phased camera onboarding.

If you want, I can generate specific configuration snippets (camera registration API calls, example lifecycle policy JSON for S3, or a Kubernetes manifest for HydraVision components).