Real-Time Video Pipelines for Scalable AI Analytics
UnicPulse uses DeepStream-based pipelines to ingest, decode, process, and analyze live video streams at scale with low latency and high throughput.
Stream Mode
Live
RTSP, cameras, files
Pipeline
Unified
Decode to output
Scale
Multi
High-throughput feeds
Unified video processing from ingestion to output.
DeepStream Pipelines form the backbone of UnicPulse Video Intelligence, enabling end-to-end processing of video streams inside a unified, optimized pipeline.
DeepStream Pipelines
Real-time video analytics path
How It Works
An optimized video stream path
DeepStream processes live video through decoding, preprocessing, inference, post-processing, and output stages.
Video Ingestion
Captures streams from cameras, RTSP feeds, or video files.
Decoding
Efficiently decodes video streams using hardware acceleration.
Preprocessing
Resizes, normalizes, and prepares frames for model input.
AI Inference
Runs detection, classification, or tracking models on frames.
Post-Processing
Applies filtering, tracking logic, and event detection.
Output Delivery
Streams results to dashboards, APIs, or storage systems.
Core Capabilities
Video analytics pipelines built for scale
DeepStream keeps video workloads efficient by integrating every stage of stream processing into a single optimized path.
Multi-Stream Processing
Handle multiple video feeds simultaneously with consistent performance.
Low-Latency Execution
Process frames in real time with minimal delay.
End-to-End Pipeline Integration
Combine decoding, inference, and output within a single pipeline.
Efficient Memory Handling
Minimize data movement using optimized pipeline architecture.
Scalable Video Analytics
Scale from single-camera setups to large multi-camera systems.
Technology Integration
DeepStream connected to the acceleration stack
UnicPulse combines DeepStream with CUDA, TensorRT, and Triton to keep video analytics fast, efficient, and deployable at scale.
CUDA Acceleration
Parallel processing for decode, preprocessing, and frame workloads.
TensorRT
Optimized inference for low-latency detection and classification.
Triton Inference Server
Model serving for scalable deployment across video analytics systems.
High-performance execution without extra pipeline complexity.
The integrated stack helps DeepStream pipelines keep processing fast, resource-efficient, and ready for scalable deployment.
DeepStream Pipelines
Real-time video analytics path
Performance Characteristics
Pipeline performance for continuous video workloads
DeepStream helps keep each stage of video processing efficient, from decoding through analytics output.
Metric 01
Real-time video processing
Metric 02
High throughput for multiple streams
Metric 03
Reduced latency across pipeline stages
Metric 04
Efficient GPU utilization
Use Case Integration
Real-time video analytics across live environments
DeepStream supports the video workloads that need fast detection, tracking, monitoring, and alerting.
Smart Surveillance
Detect and track objects across multiple camera feeds.
Traffic Monitoring
Analyze vehicle movement and traffic patterns in real time.
Retail Analytics
Track customer behavior and store activity.
Industrial Safety
Monitor environments and detect safety violations.
Deployment Flexibility
Cloud, edge, and hybrid video pipelines.
Integration Capabilities
Analytics outputs where operations need them.
Scalability and Reliability
Stable processing for high-volume stream load.
Video data is one of the most demanding AI workloads.
Without optimized pipelines, achieving real-time video intelligence is difficult. DeepStream enables efficient end-to-end video processing with less system complexity.
DeepStream Pipelines
Real-time video analytics path
Build scalable, real-time video analytics systems with UnicPulse.
Ingest, decode, process, analyze, and deliver AI-powered video outputs through optimized DeepStream pipelines.