Computer Vision & Video Analytics

Phase 1 establishes the video ingestion backbone. We audit existing camera infrastructure — IP cameras, USB industrials, thermal imagers — documenting resolution, frame rate, codec support, and network bandwidth per stream. DeepStream pipeline configurations are authored to decode, batch, and pre-process multiple streams in parallel on a single GPU, using hardware-accelerated NVDEC for H.264/H.265 decoding. Stream multiplexing logic handles heterogeneous camera types, normalizing resolution and color space before inference. Network architecture ensures reliable delivery: RTSP with TCP fallback for on-premises cameras, WebRTC for remote edge nodes, and adaptive bitrate for bandwidth-constrained links. We implement stream health monitoring that detects frozen frames, bitrate anomalies, and connection drops, automatically reconnecting and alerting operators. Edge compute nodes are sized based on total stream count, model complexity, and latency requirements — balancing Jetson AGX for dense-camera sites against T4 instances for cloud-processed feeds. Storage policies define retention windows for raw video (compliance-driven) and metadata (analytics-driven). Deliverables include the DeepStream ingestion configuration, network topology documentation, edge-compute sizing recommendations, stream-health monitoring dashboards, and a camera onboarding run-book for adding new feeds without pipeline downtime.

Phase 2 selects and optimizes the inference models that power analytics. We evaluate pre-trained models from NVIDIA's NGC catalog and open-source repositories against the client's detection requirements — people counting, vehicle classification, PPE compliance, defect detection — using a representative validation dataset collected from the deployed cameras. Models are benchmarked on accuracy (mAP, F1), latency (end-to-end per-frame), and throughput (streams per GPU) on the target inference hardware. Selected models are optimized through TensorRT: layer fusion, kernel auto-tuning, and precision calibration (FP16/INT8) with calibration datasets that preserve accuracy within acceptable bounds. We run quantization-aware fine-tuning when INT8 calibration introduces unacceptable accuracy loss on tail classes. Multi-model ensembles are evaluated where single-model accuracy is insufficient — cascaded detectors with a fast first-stage and accurate second-stage, or task-specific heads sharing a common backbone. Inference plugins are packaged as DeepStream-compatible GStreamer elements with configurable batch sizes and dynamic batching policies. Deliverables include optimized TensorRT engine files per target platform, accuracy/latency benchmark reports, precision-calibration datasets, and a model-update playbook documenting the re-optimization workflow when new model versions become available.

Phase 3 composes the end-to-end analytics application. DeepStream graph pipelines chain inference, tracking, and classification stages — primary detection feeds a multi-object tracker (NvDCF or DeepSORT), which feeds secondary classifiers for attribute extraction (color, type, action). The Metropolis application framework wraps these pipelines into microservices with REST/gRPC APIs, enabling integration with enterprise systems — access control, warehouse management, quality assurance platforms. We implement business-rule engines that translate raw detections into actionable events: dwell-time thresholds for loitering alerts, zone-crossing logic for occupancy counting, trajectory analysis for traffic-flow optimization. Alert routing delivers notifications through configurable channels — MQTT for IoT platforms, webhooks for n8n automation, email/SMS for operator escalation — with severity classification and deduplication logic to prevent alert fatigue. Geo-spatial contextualization maps detections to facility floor plans, enabling location-aware analytics. Pipeline health metrics — inference latency, tracker ID switches, dropped frames — feed into Grafana dashboards for operational monitoring. Deliverables include deployed DeepStream pipeline configurations, Metropolis microservice containers, alert-routing rule sets, integration adapters, and operational dashboards. This production system provides the analytics foundation that Phase 4 wraps with operator interfaces and continuous improvement workflows.

The final phase delivers operational value and establishes continuous improvement. We build operator dashboards that overlay detection results, heat maps, and trend charts onto facility maps and live camera feeds, providing situational awareness without requiring analysts to interpret raw model outputs. Role-based views tailor the display: security operators see alert queues with video evidence clips, quality engineers see defect trend charts with drill-down to individual detections, and facility managers see aggregate KPI scorecards. A human-in-the-loop review interface lets operators confirm or correct model predictions on flagged edge cases, generating curated training examples that feed back into model improvement. These corrections are batched into retraining datasets, triggering automated fine-tuning pipelines that produce updated TensorRT engines with measurable accuracy improvements. We implement A/B testing infrastructure that deploys candidate models alongside production models, comparing metrics on live traffic before promoting updates. Model-performance monitoring tracks accuracy drift over time — seasonal lighting changes, new product SKUs, altered camera angles — and triggers retraining alerts when KPIs degrade below thresholds. Deliverables include the operator dashboard application, human-in-the-loop review tool, retraining automation pipeline, A/B testing framework, and a model-lifecycle management run-book.