E2C
CCTV
SIH
CCTV-Survillence // E2C

Intelligent Border Video Analytics

Zero-latency threat triage converting standard border optical & thermal feeds into actionable defense intelligence. Powered by an advanced Vision-Language architecture.

Launch Live Demo View Presentation

Massive Scale Capabilities

Engineered for border security command centers, fusing cutting-edge AI with ultra-low latency streaming protocols.

πŸ‘οΈ

Hardware-Agnostic Edge

Integrates instantly with existing IP (RTSP), ONVIF, and drone links without requiring dedicated proprietary smart cameras. Works on standard mini-PCs.

🧠

Zero-Shot Grounding

Powered by the LocateAnything-3B VLM. Execute natural language queries like "unattended cargo" or "armed trespasser" without retraining models.

⚑

Parallel Box Decoding

Bypasses sequential text generation bottlenecks. Our PBD engine extracts bounding boxes 2.5Γ— faster, achieving 12.7+ targets per second on edge hardware.

πŸ—ΊοΈ

Interactive Geofencing

Draw virtual tripwires and exclusion zones directly on the video feed. Real-time vector math triggers alerts instantly upon polygon intersection.

πŸ”

Persistent Tracking

ByteTrack & Kalman filtering ensure targets maintain their identities even through dense dust, foliage, or temporary camera occlusions.

πŸ“Š

Unified C2 Dashboard

A single React/Vite command center. Replaces disparate pipelines (YOLO, OCR, FaceRec) with one unified threat timeline and telemetry HUD.

Pitch Presentation

Review the end-to-end architecture, technical methodologies, and SIH impact statement.

πŸ“„ Slide Deck View
⬇ Download PDF

Live Command Interface

Interact with the SENTINEL-3B tactical frontend. Explore geofencing, threat logs, and telemetry.

πŸ”΄ Live Terminal Preview
β†— Open in New Tab

Our Team

Meet the people behind this project.

Arunesh

Arunesh

B. Tech CSE (AI & ML) | 2nd year

Team Lead

Dinesh

Dinesh

B. Tech CSE (AI & ML) | 2nd year

RND Group

Haris

Haris

B. Tech CSE (AI & ML) | 2nd year

RND Group

Adithya

Adithya

B. Tech CSE (AI & ML) | 2nd year

Documentation Group

Lithika

Lithika

B. Tech CSE (AI & ML) | 2nd year

Presentation Group

Nandhini

Nandhini

B. Tech CSE (AI & ML) | 2nd year

Presentation Group

Technical Documentation

Essential architecture, deployment, and integration reference β€” condensed for quick review.

Pipeline

  • RTSP / ONVIF / file ingest via OpenCV
  • VLM inference with parallel box decoding
  • Kalman tracking + geofence breach logic
  • Live overlays & alerts over WebSocket

Quick deploy

  • Clone repo & pull model weights
  • uvicorn main:app --port 8000 in backend
  • npm run dev in frontend
  • Windows: use start_project.bat

Performance profiles

Profile Latency Resolution VRAM
Fast ~14–18 ms 640Γ—640 7.8 GB
Hybrid (default) ~22–32 ms 1024Γ—1024 11.7 GB
Slow / forensic ~60–85 ms 4K 18.2 GB

API

Stream: ws://host:8000/ws/stream

JSON payloads include camera ID, prompts, geofence polygons, frame tracks, and severity alerts.

SIH PS 26187

  • Human & vehicle localization
  • Perimeter violation detection
  • Night / thermal-ready ingest
  • Unified C2 integration

How to Use

Getting started with the E2C platform is simple. Clone the repository, download the VLM weights, and launch the servers.

System Requirements

  • Python 3.10+
  • Node.js 18+
  • NVIDIA GPU (RTX 3080+)
  • CUDA 12.1+
Terminal
# 1. Clone repository and initialize Git LFS
git clone https://github.com/lenluarun/E2C-CCTV.git
cd E2C-CCTV
git lfs install
# 2. Acquire LocateAnything-3B weights
huggingface-cli download nvidia/LocateAnything-3B --local-dir LocateAnything-3B
# 3. Setup Python Backend
cd backend
python -m venv venv
# Windows: .\venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --host 0.0.0.0 --port 8000
# 4. Setup React Frontend
cd ../frontend
npm install
npm run dev