System Requirements#

This page provides detailed hardware, software, platform requirements, and supported models to help you set up and run the application efficiently.

Software and Hardware Requirements#

  • OS: Windows 11

  • Recommended processor: Intel® Core Ultra Series 1, 2, and 3 Processors (with integrated GPU support)

  • Memory: 32 GB RAM (minimum recommended)

  • Storage: At least 50 GB free (for models and logs)

  • GPU/Accelerator: Intel® iGPU (Core Ultra Series 1, Arc GPU, or higher) for summarization acceleration

  • NPU: Intel® NPU (Core Ultra Series 1 or higher) for Video pipelines

  • NPU Driver: Please download and install the latest version from Intel NPU Driver Download Page

  • Python: 3.12

  • Node.js: v18+ (for frontend)

Audio Pipeline Supported Models#

ASR (Automatic Speech Recognition)#

  • Whisper (all models supported)

    • Recommended: whisper-small or lower for CPU efficiency

    • Runs on CPU (Whisper is CPU-centric)

  • FunASR (Paraformer)

    • Recommended for Chinese transcription (paraformer-zh)

  • Supports transcription of .mp3/.wav audio files up to 45 minutes long.

Summarization (LLMs)#

  • Qwen Models (OpenVINO / IPEX)

    • Qwen2.0-7B-Instruct

    • Qwen2.5-7B-Instruct

  • Summarization supports up to 7,500 tokens (≈ 45 minutes of audio) on GPU

  • Run summarization on GPU (Intel® iGPU / Arc GPU) for faster performance.

Content Segmentation and Topic Search#

  • Embedding Model: BAAI/bge-large-en-v1.5 for semantic topic indexing and search

  • Vector Store: FAISS (IndexFlatIP with cosine similarity)

  • Content segmentation uses the same LLM as summarization (e.g., Qwen2.5-7B-Instruct)

Supported Weight Formats#

  • int8 → Recommended for lower-end CPUs (fast + efficient)

  • fp16 → Recommended for higher-end systems (better accuracy, GPU acceleration)

  • int4 → Supported, but may reduce accuracy (use only if memory-constrained)

Video Analytics Pipeline#

  • Supports 3 concurrent video pipelines (front, back, content) up to 45 minutes

  • Supports .mp4 format and RTSP streams

  • Outputs processed video via RTSP and HLS/WebRTC streaming (MediaMTX)

For pipeline architecture and processing stages, see How It Works.

Supported Models#

Model

Format

Used In

Purpose

YOLOv8m-pose

OpenVINO IR

Front pipeline

Person detection + 17-keypoint pose estimation

YOLOv8s-pose

OpenVINO IR

Back pipeline

Lightweight person detection + pose estimation

ResNet-18

OpenVINO IR

Front, Back, Content

Activity/action classification

MobileNet-V2

OpenVINO IR

Front pipeline

Lightweight classification

Person-ReID-retail-0288

OpenVINO IR

Front pipeline

Person re-identification and tracking

  • All models run in OpenVINO Intermediate Representation (IR) format

  • Inference supported on CPU, GPU, and NPU (configurable per pipeline)

  • Default inference device: NPU (recommended for best performance on Intel® Core Ultra)

Content Search Pipeline#

Content Search Supported Models#

Model

Purpose

Device

Qwen2.5-VL-3B-Instruct

Vision Language Model for video summarization

GPU

xlm-roberta-base-ViT-B-32 (CLIP)

Visual embedding for images and video frames

CPU

BAAI/bge-small-en-v1.5

Text embedding for document chunks

CPU

BAAI/bge-reranker-large

Cross-encoder reranking for search results

GPU

Supported File Formats#

Category

Extensions

Video

.mp4

Document

.txt, .pdf, .docx, .doc, .pptx, .ppt, .xlsx, .xls, .html, .htm, .xml, .md

Image

.jpg, .jpeg, .png