Using Predefined Pipelines

The application includes several predefined pipelines. These pipelines cover common use cases and can be customized to fit specific requirements.

Pipeline	Description	Variants
	Age & Gender Recognition: Retail analytics pipeline using face-detection-retail-0004 for face detection and age-gender-recognition-retail-0013 for age and gender prediction. It is suited for customer demographics analysis and similar retail use cases.	Available in CPU, GPU, NPU, and GPU+NPU variants.
	Defect Detection: AI-powered pallet defect detection pipeline for manufacturing quality control using machine vision.	Available in CPU, GPU, and NPU variants.
	Goods Detection: Retail pipeline using YOLO 11n object detection to identify retail-related objects for inventory management, customer behavior analysis, and similar use cases.	Available in CPU, GPU, and NPU variants.
	Goods Detection & Classification: Retail pipeline using YOLO 11n for detection and EfficientNet B0 for classification to identify and categorize retail-related objects.	Available in CPU, GPU, NPU, and GPU+NPU variants.
	License Plate Recognition: Simple Video Structurization (D-T-C) pipeline that supports license plate recognition, vehicle detection with attribute classification, and other adaptable detection and classification tasks based on the selected model.	Available in CPU, GPU, NPU, and GPU+NPU variants.
	Motion Detection: Pipeline that uses gvamotiondetect to identify motion regions, then runs YOLOv8n object detection restricted to those motion ROIs through gvadetect.	Available in CPU, GPU, and NPU variants.
	Simple NVR: Lightweight media pipeline for basic video decoding, recording, and format conversion.	Available in CPU and GPU variants.
	Smart NVR: Video analytics pipeline that combines recording with AI-based object detection, tracking, and classification, and produces metadata and processed video frames.	Available in CPU, GPU, NPU, and GPU+NPU variants.
	Smart Parking: Cloud-native video analytics pipeline that uses pre-trained deep learning models to detect parking-space occupancy.	Available in CPU, GPU, NPU, and GPU+NPU variants.
	Video Summarization VLM: Pipeline using gvagenai with a vision-language model to generate concise, scene-level summaries from sampled frames.	Available in CPU, GPU, and NPU variants.
	Segmentation: This is a segmentation pipeline preview. Segmentation is used to identify individual objects and separate them from the original image.	Available in CPU and GPU variants.
	Human Pose Detection: This is a classic physical security use case that detects human poses in real-time. It is particularly useful in use cases such as slip-and-fall detection in elderly care facilities, hospitals, and public spaces.	Available in CPU and GPU variants.