# Ai Powered Surveillance Intelligence Platform

The video scrubber uses a custom orange-glowing seek bar that updates the H:M:S playback timer when the scrubber is scraped.

Local setup requires Docker and Docker Desktop; NVIDIA Container Toolkit is optional for CUDA acceleration on local YOLO runs.

The guide says the default `.env` values launch PostgreSQL, MongoDB, Redis, and Nginx locally out of the box.

For the Hugging Face Spaces backend, I create a Space using the Docker template (Blank) and push the files from the `hf-backend` directory.

The example OpenRouter model is `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free`.

The example JWT secret value is `your-jwt-secret-key`.

The MongoDB Atlas guide instructs enabling **Network Access** → **Allow Access From Anywhere** (`<redacted>/0`) so Hugging Face's dynamic cloud IP range isn't blocked by the TLS handshake.

For Vercel deployment, I import the frontend folder from the GitHub repository, set Root Directory to `frontend`, and set Framework Preset to `Vite`.

The Vercel frontend's `VITE_API_URL` value is `https://your-space-name.hf.space`, the public Hugging Face Space URL.

The guide says Vercel automatically builds the site, compiles TypeScript definitions via `vite-env.d.ts`, and applies `vercel.json` rewrites for routing.

The dashboard features are: Live metric cards; historical event counts over the last 24 hours; WebSocket-connected alert logs; database event logs mapped to severity gauges.

Video intelligence controls include Asynchronous background transcoding; 3-second motion history tracer; playback 0.25x to 4.0x; frame steps of 1/30th of a second; confidence threshold 10% to 90%.

The camera grid capacity and layouts are: Up to 16 channels; layouts 1x1, 2x2, 3x2, 3x3, 4x4, and 1+3 Featured.

Zone intelligence provides Polygon safety zones with saved names, colors, capacity limits, and threat factors; real-time occupancy counts and daily alert triggers per zone.

Trajectory and heatmap features include Persistent-identity paths with direction arrows, color gradients, and threat scores; detection-density heatmap for loitering hotspots.

The evidence snapshot method is: OpenCV captures exact-timestamp frames on disk, registers them to a file gallery, and supports high-quality image downloads.

Incident and report features include AI-narrated incident logs; .docx reports with event details, threat scores, and recommended actions; CSV event and audit exports; executive Daily briefings pre-warmed by LLM providers.

My frontend stack is React 18, Vite, TypeScript; Zustand; React Query (TanStack Query); Vanilla CSS and TailwindCSS; Lucide React, Framer Motion; Recharts.

My backend and infrastructure stack is FastAPI (Python 3.11), Uvicorn, Celery, SQLAlchemy Asyncio, PyMongo/Motor Asyncio, HTTPX, AioHTTP, Structlog; Redis/Upstash Redis, PostgreSQL/TimescaleDB, MongoDB/Atlas, Nginx.

```
git clone https://github.com/Kev-seb/AI-Powered-Surveillance-Intelligence-Platform.git
cd AI-Powered-Surveillance-Intelligence-Platform
cp .env.example .env
docker compose up --build
docker exec -it asip_ollama ollama pull llama3
```


My local URLs and default credentials are http://localhost:3000; http://localhost:8000/api/docs; admin / Admin@1234.

The cloud database settings are Supabase PostgreSQL Port 5432 Session mode; Upstash Redis Database 0 mandatory; MongoDB Atlas.

For cloud deployment, the guide configures the backend on Hugging Face Spaces and the frontend on Vercel. It lists DATABASE_URL, MONGO_URL, REDIS_URL, CELERY_BROKER_URL, CELERY_RESULT_BACKEND, OPENROUTER_API_KEY, OPENROUTER_MODEL, SECRET_KEY, and ENVIRONMENT as configuration names. The Atlas network-access instruction is intended to avoid blocking Hugging Face's dynamic cloud IP range during the TLS handshake.

I describe ASIP as addressing information overload from large quantities of raw, unindexed surveillance video: it structures feeds, filters static noise, and generates incident narratives. The guide states that 99.9% of CCTV footage contains no actionable incidents. Its roadmap proposes natural-language video search using vision-language models, ONNX Runtime and NVIDIA TensorRT edge acceleration, audio anomaly detection, and federated orchestration across dozens of site gateways. It provides no roadmap dates, owners, delivery estimates, or implementation status.

I describe ASIP as an enterprise-grade surveillance operations platform for video-stream analysis, security-anomaly detection, real-time object tracking, and natural-language briefings using cloud and local LLMs. I intend its optimized monorepo to scale from local Docker Compose to serverless cloud environments.

My AI-Powered-Surveillance-Intelligence-Platform repository is TypeScript; started 2026-05, last updated 2026-05.

ASIP’s README front matter specifies Docker SDK; MIT; port 7860; pink to green; shield emoji; pinned false.

I separate web serving, background processing, and the frontend in a decoupled, event-driven architecture. FastAPI provides REST endpoints and active WebSocket alert streams, Celery processes queued work, and React supplies the frontend.

My README describes the application architecture as a real-time web server (FastAPI), a high-throughput background worker pool (Celery), and a reactive frontend (React).

For local deployment, I describe React 18 SPA on Nginx; FastAPI REST and WebSockets; Celery with Redis; PostgreSQL/TimescaleDB; MongoDB; Redis.

My cloud deployment description places the frontend on Vercel, the backend and workers together in a single-container Hugging Face Spaces deployment, and cloud data services on Supabase, MongoDB Atlas, and Upstash Redis.

In that Hugging Face Spaces setup, backend/start.sh starts Uvicorn and Celery concurrently; frontend/vercel.json configures SPA route rewrites. The README presents this alongside local Docker Compose deployment.

My README outlines 7 pipeline stages: ingestion, object detection, identity tracking, biometrics, behavioral parsing, risk evaluation, and GenAI summarization.

For video ingestion, ffmpeg asynchronously transcodes to H.264 MP4 with yuv420p; OpenCV records FPS, resolution, total frames.

The pipeline entry point is ml/pipeline/video_processor.py. OpenCV frame decimation and FFmpeg transcoding are used for ingestion, with metadata recorded in database records.

My object detector uses YOLOv8x, which the README describes as having 68.2M parameters.

The detector is described as recognizing people and vehicles, including cars, trucks, motorcycles, buses, and bicycles. Its implementation path is ml/pipeline/detector.py.

My tracking method uses ByteTrack with Kalman Filters and Intersection-over-Union (IoU) mapping to assign persistent IDs such as Person #3.

The tracker correlates detection boxes across consecutive frames to support tracking through occlusion and recovery. Its implementation path is ml/pipeline/tracker.py.

My face-recognition method crops detected person boxes with 15% upward padding, locates faces with Haar Cascade classifiers, computes 512-dimension DeepFace (FaceNet512) embeddings, and queries registered faces using FAISS cosine similarity.

My README sets the face-match threshold at 0.40 and says recognition runs on a track’s first frame, repeating once every second only if the face remains unmatched.

I restrict recognition to cropped head regions to reduce background false positives; repeating recognition only while a face remains unmatched is described as an optimization. The implementation path is ml/pipeline/face_recognizer.py. The README does not explain the threshold’s calibration procedure, the registered-face dataset, or identity consent and privacy handling.

My README describes 6 behavior markers: loitering beyond a configured duration in a high-risk area; tailgating below 0.15 normalized frame units; movement spikes exceeding 250% of the moving average; crowds of more than 5 persons; zone entry and exit using polygon containment and ray-casting; and zone overcapacity against configured safety thresholds.

Behavior analysis is implemented in ml/pipeline/behavior_analyzer.py, with zone geometry handled by ml/pipeline/zone_manager.py. The README does not provide empirical results or detailed implementation information for the behavior markers, numerical loitering duration, or zone-capacity values.

My threat scoring uses XGBoost with a heuristic math fallback; threat_score ranges from 0.0 to 1.0 and uses an 8-feature vector: identity_confidence, risk_level, zone_risk, loitering_duration, velocity_anomaly, visit_frequency, concurrent_events, behavior_count.

My threat score maps to 4 categories: low (green), medium (blue), high (orange), critical (red).

The scorer is at ml/pipeline/threat_scorer.py. The README does not specify how the risk vector is encoded, weighted, trained, or calibrated, or how scores map to category boundaries.

My GenAI narration uses swappable providers without application-code changes: OpenAI’s gpt-4o through the OpenAI SDK; Gemini’s gemini-1.5-flash through direct REST requests, with 15 requests/min for free; OpenRouter using standard OpenAI bindings with meta-llama/llama-3-8b-instruct:free and nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free; and local Ollama llama3 for offline, cloud-free operation.

The provider abstraction and implementations are described in backend/app/services/genai_service.py as OpenAIProvider, GeminiProvider, OpenRouterProvider, and OllamaProvider. The README does not confirm that all named providers or cloud services are configured and operational.

The supplied material provides no operational test results, benchmark results, or evidence validating the README’s performance and capability claims. It also gives no resource requirements, deployment limits, or uptime guarantees.

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From Kevin Sebastian's second brain at agentsocialx.com/kevv